Jason Locke

Deep Learning HW1

10-4-2020

Problem 1

Imports

In [ ]:
import numpy as np
import pandas as pd
from tensorflow import keras
import tensorflow as tf
from tensorflow.keras import layers
from matplotlib import pyplot as plt
from keras import backend as kb
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import seaborn as sns
%matplotlib inline

Load Data into Numpy Arrays

In [ ]:
#Using Keras load_data extract values for mnist into train and test values
#(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()

#Using Keras load_data extract values for mnist into train and test values
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
X_train, X_test = X_train/255.0, X_test/255.0
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
In [ ]:
#What does the data look like?
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
(60000, 28, 28)
(60000,)
(10000, 28, 28)
(10000,)
In [ ]:
# What does a sample image look like?  This is the 10,000th indexed value.  This looks like 3.
plt.imshow(X_train[10000], cmap='gray')
plt.show()
In [ ]:
# Is the label activated for 10,000 index to show 3?
y_train[10000]
Out[ ]:
3

The label shows 3, but this is a problem. We need to have categorical values of 0's and 1's as oposed to the actual labels.

In [ ]:
#Use Keras utility to transform labels into onehot encoding (binary categories)
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
In [ ]:
# This looks like a three, lets check label for the 10,000th index value.  Looks like the 3rd value (4th index) was activated.
y_train[10000]
Out[ ]:
array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], dtype=float32)

Create DL Model

In [ ]:
shape = (28, 28, 1) # Define shape of input for Keras model

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(1024,activation='relu'),
        tf.keras.layers.Dense(1024,activation='relu'),
        tf.keras.layers.Dense(1024,activation='relu'),
        tf.keras.layers.Dense(1024,activation='relu'),
        tf.keras.layers.Dense(1024,activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
    ]
)

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 784)               0         
_________________________________________________________________
dense (Dense)                (None, 1024)              803840    
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_3 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_4 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_5 (Dense)              (None, 10)                10250     
=================================================================
Total params: 5,012,490
Trainable params: 5,012,490
Non-trainable params: 0
_________________________________________________________________

Compile and Fit DL Model

In [ ]:
#Define model
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

#Fit model
model.fit(X_train, y_train, batch_size=64, epochs=10, validation_split=0.1)
Epoch 1/10
844/844 [==============================] - 3s 4ms/step - loss: 0.2430 - accuracy: 0.9309 - val_loss: 0.1202 - val_accuracy: 0.9655
Epoch 2/10
844/844 [==============================] - 3s 4ms/step - loss: 0.1177 - accuracy: 0.9682 - val_loss: 0.0974 - val_accuracy: 0.9738
Epoch 3/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0901 - accuracy: 0.9757 - val_loss: 0.0945 - val_accuracy: 0.9753
Epoch 4/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0732 - accuracy: 0.9810 - val_loss: 0.0824 - val_accuracy: 0.9777
Epoch 5/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0599 - accuracy: 0.9836 - val_loss: 0.1060 - val_accuracy: 0.9755
Epoch 6/10
844/844 [==============================] - 3s 3ms/step - loss: 0.0561 - accuracy: 0.9855 - val_loss: 0.1002 - val_accuracy: 0.9757
Epoch 7/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0416 - accuracy: 0.9890 - val_loss: 0.0949 - val_accuracy: 0.9800
Epoch 8/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0374 - accuracy: 0.9898 - val_loss: 0.0728 - val_accuracy: 0.9808
Epoch 9/10
844/844 [==============================] - 3s 4ms/step - loss: 0.0348 - accuracy: 0.9904 - val_loss: 0.1107 - val_accuracy: 0.9767
Epoch 10/10
844/844 [==============================] - 3s 3ms/step - loss: 0.0354 - accuracy: 0.9910 - val_loss: 0.1072 - val_accuracy: 0.9790
Out[ ]:
<tensorflow.python.keras.callbacks.History at 0x7f26a00f7ef0>

Evaluate Test Data

In [ ]:
score = model.evaluate(X_test, y_test, verbose=1)
313/313 [==============================] - 1s 2ms/step - loss: 0.1078 - accuracy: 0.9811

Create Function to extract layer data

In [ ]:
def extract_layer_output(data, layer_index):
  layer_output = []
  keras_function = kb.function([model.input], [model.get_layer(index=layer_index).output])
  layer_output.append(keras_function([data, 1]))
  layer_output = np.squeeze(layer_output) # remove all dimensions of size 1 i.e., (1,1,10000,1024) to (10000, 1024)
  return(layer_output)

Test Data Analysis

In [ ]:
#Get 1000 samples from test data
X_test_1000 = X_test[0:1000]
y_test_1000 = y_test[0:1000]

Softmax Predictions

In [ ]:
#Get softmax layer output for 1000 test samples
softmax_output = extract_layer_output(X_test_1000, 6) #6 is last softmax layer

#Get max index for each matrix (convert from matrix of 10 labels to 1 which is the prediction)
softmax_output = np.argmax(softmax_output,axis=1)
In [ ]:
softmax_output.shape
Out[ ]:
(1000,)
In [ ]:
#Get the first 10 predictions for each nbr from 0-10.  First 10 are 0, then 1, etc..
l = []
for x in range(0,10):
    t = np.where(softmax_output==x)
    t = np.array(t)
    t = np.squeeze(t)
    t = t[0:10]
    l.append(t)
l = np.array(l)
l = l.reshape(100)
In [ ]:
print(l)
[  3  10  13  25  28  55  69  71 101 126   2   5  14  29  31  37  39  40
  46  57   1  35  38  43  47  72  77  82 106 119  18  30  32  44  51  63
  68  76  87  90   4   6  19  24  27  33  42  48  49  56   8  15  23  45
  52  53  59 102 120 127  11  21  22  50  54  66  81  88  91  98   0  17
  26  34  36  41  60  64  70  75  61  84 110 128 134 146 177 179 181 184
   7   9  12  16  20  58  62  73  78  92]
In [ ]:
#Create 10X10 plot that shows predictions for first 10 of each nbr.  If predictions are good all nbrs should match on each row
num_row = 10
num_col = 10

fig = plt.figure
fig, axes = plt.subplots(num_row, num_col, figsize=(1.5*num_col,2*num_row))
for i,tt in enumerate(l):
    ax = axes[i//num_col, i%num_col]
    ax.imshow(X_test_1000[tt], cmap='gray')
    ax.set_xticks([])
    ax.set_yticks([])

plt.show()

Second to Last Layer Predictions

In [ ]:
#Get softmax layer output for 1000 test samples
secondlast = extract_layer_output(X_test_1000, 5) #5 is the second to last layer (last hidden)

#Get max index for each matrix (convert from matrix of 10 labels to 1 which is the prediction)
#secondlast = extract_layer_output(X_test_1000, 5) #6 is current last softmax layer
In [ ]:
#Select 10 random int's
int = np.random.randint(0,secondlast.shape[1],10)
int = np.sort(int)
In [ ]:
secondlast = secondlast[:,int]
In [ ]:
#Get max index for each matrix (convert from matrix of 10 labels to 1 which is the prediction)
secondlast = np.argmax(secondlast,axis=1)
In [ ]:
#Get the first 10 predictions for each nbr from 0-10.  First 10 are 0, then 1, etc..
l = []
for x in range(0,10):
    t = np.where(secondlast==x)
    t = np.array(t)
    if t.size < 10:
      t = np.full(10,-1)
    t = np.array(t)
    t = np.squeeze(t)
    t = t[0:10]
    l.append(t)
l = np.array(l)
l = l.reshape(100)
In [ ]:
#Create 10X10 plot that shows predictions for first 10 of each nbr.  If predictions are good all nbrs should match on each row
num_row = 10
num_col = 10

fig = plt.figure
fig, axes = plt.subplots(num_row, num_col, figsize=(1.5*num_col,2*num_row))
for i,tt in enumerate(l):
    ax = axes[i//num_col, i%num_col]
    #print(tt)
    if tt==-1:
      ''
    else:
      ax.imshow(X_test_1000[tt], cmap='gray')
    ax.set_xticks([])
    ax.set_yticks([])

plt.show()

Analysis

These results were confusing to me and really dont make sense. Since the 10 of the 1024 dimensions are chosen at random there is no way to tie them back to actual labels. The results are very random and do not help explain relationships in the data compared to the actual labels. It does appear to somewhat group results, but is not clear to me how this would be helpful.

Apply TSNE to reduce and visualize dimensions

In [ ]:
#Create funtion to generate DF's for dimensions and means for visualization 
def createDF(data, labels):
  df_test = pd.DataFrame({'x': data[:, 0], 'y': data[:, 1], 'label': labels})
  df_mean = df_test.groupby('label').mean()
  return df_test, df_mean
In [ ]:
#Convert test labels to true values by index
test_labels = np.argmax(y_test_1000,axis=1)

Raw Data - 1000 examples

In [ ]:
#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(X_test_1000.reshape(1000,784))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

First Layer - 1000 examples

In [ ]:
#Get first layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 1)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,1024))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

2nd Layer - 1000 examples

In [ ]:
#Get second layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 2)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,1024))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

3rd Layer - 1000 examples

In [ ]:
#Get third layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 3)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,1024))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

4th Layer - 1000 examples

In [ ]:
#Get fourth layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 4)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,1024))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

5th Layer - 1000 examples

In [ ]:
#Get fifth layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 5)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,1024))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

Last Layer (softmax) - 1000 examples

In [ ]:
#Get last layer output for 1000 test samples
layer_data = extract_layer_output(X_test_1000, 6)

#Apply TSNE to test data
layer_data = TSNE(n_components=2).fit_transform(layer_data.reshape(1000,10))

#Call function to get DF and DF with mean values for visualization
df_test, df_mean = createDF(layer_data, test_labels)
In [ ]:
#Plot 1000 test examples using TSNE dimension reduction
p1 = sns.lmplot(data=df_test, x="x", y="y", fit_reg=False, hue="label")

for index, row in df_mean.iterrows():
    plt.annotate(index, xy=(row[0],row[1]), weight='bold', fontsize=20)

#sns.plt.show()

Analysis

I tried both PCA and TSNE and TSNE results were much easier for me to undestand. As I reduced the dimensions for each layer the groupings get closer and closer as you make it to the last layer (softmax output). Using dimension reduction does seem very helpful for understandnig the population of the data.

Problem 2

Imports

In [ ]:
import numpy as np
from tensorflow import keras
import tensorflow as tf
from tensorflow.keras import layers
import scipy
!pip install librosa # in colab, you’ll need to install this
import librosa
Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)
Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)
Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.4.1)
Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.22.2.post1)
Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.48.0)
Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.18.5)
Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.15.0)
Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)
Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.2)
Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.16.0)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (50.3.0)
Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.31.0)
In [ ]:
def readAudio(file_name):
  s, sr = librosa.load(file_name, sr=None)
  S = librosa.stft(s, n_fft=1024, hop_length=512)
  S_abs = np.abs(S).T #get absolute values and transpose
  S = S.T #transpose values

  return(S, S_abs, sr)
In [ ]:
#Read main and test audio files and convert to spectrograms
X_train, X_train_abs, sr = readAudio('train_clean_male.wav')
y_train, y_train_abs, sr = readAudio('train_dirty_male.wav')
test1, test1_abs, sr = readAudio('test_x_01.wav')
test2, test2_abs, sr = readAudio('test_x_02.wav')
In [ ]:
#Read in main audio files and convert to spectrograms
#s, sr = librosa.load('train_clean_male.wav', sr=None)
#S = librosa.stft(s, n_fft=1024, hop_length=512)
#sn, sr = librosa.load('train_dirty_male.wav', sr=None)
#X = librosa.stft(sn, n_fft=1024, hop_length=512)

#Read in test audio files and convert to spectrograms
#X, sr = librosa.load('test_x_01.wav', sr=None)
#X_test = librosa.stft(X, n_fft=1024, hop_length=512)
#X, sr = librosa.load('test_x_02.wav', sr=None)
#X_test2 = librosa.stft(X, n_fft=1024, hop_length=512)
In [ ]:
#S = np.abs(S)
#S=S.T
#X = np.abs(X)
#X=X.T
In [ ]:
#Create DNN model with 2 hidden layers
shape = (2459, 513) # Define shape of input for Keras model

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Dense(513,activation='relu'),
        tf.keras.layers.Dense(513,activation='relu'),
        tf.keras.layers.Dense(513, activation='relu')
    ]
)

model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_9 (Dense)              (None, 2459, 513)         263682    
_________________________________________________________________
dense_10 (Dense)             (None, 2459, 513)         263682    
_________________________________________________________________
dense_11 (Dense)             (None, 2459, 513)         263682    
=================================================================
Total params: 791,046
Trainable params: 791,046
Non-trainable params: 0
_________________________________________________________________
In [ ]:
#Compile and fit the model
model.compile(loss="MeanSquaredError", optimizer="adam", metrics=["accuracy"])

model.fit(X_train_abs, y_train_abs, batch_size=50, epochs=50, validation_split=0.1)
Epoch 1/50
WARNING:tensorflow:Model was constructed with shape (None, 2459, 513) for input Tensor("input_3:0", shape=(None, 2459, 513), dtype=float32), but it was called on an input with incompatible shape (None, 513).
WARNING:tensorflow:Model was constructed with shape (None, 2459, 513) for input Tensor("input_3:0", shape=(None, 2459, 513), dtype=float32), but it was called on an input with incompatible shape (None, 513).
43/45 [===========================>..] - ETA: 0s - loss: 0.0662 - accuracy: 0.1944WARNING:tensorflow:Model was constructed with shape (None, 2459, 513) for input Tensor("input_3:0", shape=(None, 2459, 513), dtype=float32), but it was called on an input with incompatible shape (None, 513).
45/45 [==============================] - 0s 5ms/step - loss: 0.0659 - accuracy: 0.2015 - val_loss: 0.0425 - val_accuracy: 0.3537
Epoch 2/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0373 - accuracy: 0.4076 - val_loss: 0.0380 - val_accuracy: 0.3740
Epoch 3/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0316 - accuracy: 0.4510 - val_loss: 0.0359 - val_accuracy: 0.4553
Epoch 4/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0280 - accuracy: 0.5011 - val_loss: 0.0345 - val_accuracy: 0.4593
Epoch 5/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0262 - accuracy: 0.5011 - val_loss: 0.0338 - val_accuracy: 0.4675
Epoch 6/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0247 - accuracy: 0.5368 - val_loss: 0.0326 - val_accuracy: 0.4878
Epoch 7/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0238 - accuracy: 0.5404 - val_loss: 0.0333 - val_accuracy: 0.4919
Epoch 8/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0228 - accuracy: 0.5490 - val_loss: 0.0327 - val_accuracy: 0.4715
Epoch 9/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0222 - accuracy: 0.5554 - val_loss: 0.0328 - val_accuracy: 0.4797
Epoch 10/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0217 - accuracy: 0.5630 - val_loss: 0.0331 - val_accuracy: 0.4756
Epoch 11/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0213 - accuracy: 0.5635 - val_loss: 0.0329 - val_accuracy: 0.4431
Epoch 12/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0211 - accuracy: 0.5599 - val_loss: 0.0332 - val_accuracy: 0.4797
Epoch 13/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0206 - accuracy: 0.5477 - val_loss: 0.0324 - val_accuracy: 0.4675
Epoch 14/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0199 - accuracy: 0.5626 - val_loss: 0.0331 - val_accuracy: 0.5000
Epoch 15/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0199 - accuracy: 0.5540 - val_loss: 0.0330 - val_accuracy: 0.4797
Epoch 16/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0190 - accuracy: 0.5612 - val_loss: 0.0334 - val_accuracy: 0.4837
Epoch 17/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0185 - accuracy: 0.5662 - val_loss: 0.0330 - val_accuracy: 0.4837
Epoch 18/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0173 - accuracy: 0.5635 - val_loss: 0.0329 - val_accuracy: 0.4878
Epoch 19/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0172 - accuracy: 0.5725 - val_loss: 0.0334 - val_accuracy: 0.4797
Epoch 20/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0165 - accuracy: 0.5716 - val_loss: 0.0326 - val_accuracy: 0.4797
Epoch 21/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0164 - accuracy: 0.5703 - val_loss: 0.0329 - val_accuracy: 0.4959
Epoch 22/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0160 - accuracy: 0.5703 - val_loss: 0.0331 - val_accuracy: 0.4959
Epoch 23/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0158 - accuracy: 0.5689 - val_loss: 0.0329 - val_accuracy: 0.4878
Epoch 24/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0154 - accuracy: 0.5703 - val_loss: 0.0330 - val_accuracy: 0.4959
Epoch 25/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0151 - accuracy: 0.5725 - val_loss: 0.0328 - val_accuracy: 0.5081
Epoch 26/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0144 - accuracy: 0.5784 - val_loss: 0.0334 - val_accuracy: 0.4837
Epoch 27/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 0.5834 - val_loss: 0.0336 - val_accuracy: 0.4878
Epoch 28/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 0.5798 - val_loss: 0.0328 - val_accuracy: 0.4837
Epoch 29/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0131 - accuracy: 0.5825 - val_loss: 0.0337 - val_accuracy: 0.4878
Epoch 30/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0130 - accuracy: 0.5883 - val_loss: 0.0342 - val_accuracy: 0.5000
Epoch 31/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0127 - accuracy: 0.5838 - val_loss: 0.0338 - val_accuracy: 0.4919
Epoch 32/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0123 - accuracy: 0.5888 - val_loss: 0.0334 - val_accuracy: 0.4797
Epoch 33/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0120 - accuracy: 0.5816 - val_loss: 0.0335 - val_accuracy: 0.5000
Epoch 34/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0122 - accuracy: 0.5911 - val_loss: 0.0334 - val_accuracy: 0.4797
Epoch 35/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0118 - accuracy: 0.5811 - val_loss: 0.0338 - val_accuracy: 0.5041
Epoch 36/50
45/45 [==============================] - 0s 4ms/step - loss: 0.0117 - accuracy: 0.5897 - val_loss: 0.0337 - val_accuracy: 0.5081
Epoch 37/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0133 - accuracy: 0.5861 - val_loss: 0.0332 - val_accuracy: 0.4919
Epoch 38/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0121 - accuracy: 0.5874 - val_loss: 0.0339 - val_accuracy: 0.5041
Epoch 39/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0122 - accuracy: 0.5879 - val_loss: 0.0336 - val_accuracy: 0.4837
Epoch 40/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0109 - accuracy: 0.5865 - val_loss: 0.0335 - val_accuracy: 0.5041
Epoch 41/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0105 - accuracy: 0.5947 - val_loss: 0.0332 - val_accuracy: 0.5000
Epoch 42/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0100 - accuracy: 0.6001 - val_loss: 0.0338 - val_accuracy: 0.4837
Epoch 43/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0098 - accuracy: 0.6019 - val_loss: 0.0334 - val_accuracy: 0.5000
Epoch 44/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0095 - accuracy: 0.6028 - val_loss: 0.0337 - val_accuracy: 0.5041
Epoch 45/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0108 - accuracy: 0.5987 - val_loss: 0.0329 - val_accuracy: 0.4959
Epoch 46/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0103 - accuracy: 0.6042 - val_loss: 0.0337 - val_accuracy: 0.5163
Epoch 47/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0100 - accuracy: 0.6033 - val_loss: 0.0331 - val_accuracy: 0.5163
Epoch 48/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0092 - accuracy: 0.6078 - val_loss: 0.0335 - val_accuracy: 0.5000
Epoch 49/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0095 - accuracy: 0.6100 - val_loss: 0.0335 - val_accuracy: 0.5163
Epoch 50/50
45/45 [==============================] - 0s 3ms/step - loss: 0.0093 - accuracy: 0.6073 - val_loss: 0.0334 - val_accuracy: 0.5244
Out[ ]:
<tensorflow.python.keras.callbacks.History at 0x7f263039ad30>
In [ ]:
#Read in test audio files and convert to spectrograms
#test1, sr=librosa.load('test_x_01.wav', sr=None)
#TEST1=librosa.stft(test1, n_fft=1024, hop_length=512)
#test2, sr=librosa.load('test_x_02.wav', sr=None)
#TEST2=librosa.stft(test2, n_fft=1024, hop_length=512)
In [ ]:
#Get absolute values
#TEST1_abs = np.abs(TEST1)
#TEST2_abs = np.abs(TEST2)
In [ ]:
#Use trained models to perform predictions
test1_predict = model.predict(test1_abs)
test2_predict = model.predict(test2_abs)
WARNING:tensorflow:Model was constructed with shape (None, 2459, 513) for input Tensor("input_3:0", shape=(None, 2459, 513), dtype=float32), but it was called on an input with incompatible shape (None, 513).
In [ ]:
s1 = np.multiply((test1/test1_abs).T, np.abs(test1_predict).T)
s2 = np.multiply((test2/test2_abs).T, np.abs(test2_predict).T)
In [ ]:
scipy.signal.istft(s1)
sh_test1 = scipy.signal.istft(s1)
scipy.signal.istft(s2)
sh_test2 = scipy.signal.istft(s2)
In [ ]:
#Conver to Numpy Arrays
sh_test1 = np.array(sh_test1)
sh_test2 = np.array(sh_test2)
In [ ]:
librosa.output.write_wav('test_s_01_recons.wav', sh_test1, sr)
librosa.output.write_wav('test_s_02_recons.wav', sh_test2, sr)
In [204]:
from IPython.display import Audio
Audio('test_s_01_recons.wav')
Out[204]:
In [205]:
from IPython.display import Audio
Audio('test_s_02_recons.wav')
Out[205]:

Problem 3

SGD

Imports

In [ ]:
import numpy as np
from tensorflow import keras
import tensorflow as tf
from tensorflow.keras import layers
import matplotlib.pyplot as plt
In [ ]:
#Using Keras load_data extract values for mnist into train and test values
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
X_train, X_test = X_train/255.0, X_test/255.0
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
In [ ]:
#Use Keras utility to transform labels into onehot encoding (binary categories)
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

(a) Activation function: the logistic sigmoid function; initialization: random numbers gen-erated from the normal distribution (μ = 0, σ = 0.01)

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_8 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_48 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_49 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_50 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_51 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_52 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_53 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.SGD(learning_rate=.01)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
history = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3060 - accuracy: 0.1043 - val_loss: 2.3111 - val_accuracy: 0.1010
Epoch 2/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3059 - accuracy: 0.1059 - val_loss: 2.3042 - val_accuracy: 0.1010
Epoch 3/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3060 - accuracy: 0.1039 - val_loss: 2.3039 - val_accuracy: 0.1028
Epoch 4/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3060 - accuracy: 0.1054 - val_loss: 2.3080 - val_accuracy: 0.1028
Epoch 5/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3057 - accuracy: 0.1055 - val_loss: 2.3075 - val_accuracy: 0.1009
Epoch 6/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3058 - accuracy: 0.1063 - val_loss: 2.3084 - val_accuracy: 0.1135
Epoch 7/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3058 - accuracy: 0.1058 - val_loss: 2.3072 - val_accuracy: 0.1009
Epoch 8/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3059 - accuracy: 0.1054 - val_loss: 2.3050 - val_accuracy: 0.1135
Epoch 9/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3054 - accuracy: 0.1075 - val_loss: 2.3055 - val_accuracy: 0.1135
Epoch 10/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3057 - accuracy: 0.1062 - val_loss: 2.3052 - val_accuracy: 0.1010
Epoch 11/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3053 - accuracy: 0.1053 - val_loss: 2.3047 - val_accuracy: 0.0980
Epoch 12/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3055 - accuracy: 0.1047 - val_loss: 2.3040 - val_accuracy: 0.1135
Epoch 13/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3057 - accuracy: 0.1041 - val_loss: 2.3026 - val_accuracy: 0.1135
Epoch 14/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3050 - accuracy: 0.1061 - val_loss: 2.3061 - val_accuracy: 0.0982
Epoch 15/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3052 - accuracy: 0.1051 - val_loss: 2.3062 - val_accuracy: 0.1135
Epoch 16/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3053 - accuracy: 0.1050 - val_loss: 2.3028 - val_accuracy: 0.1135
Epoch 17/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3053 - accuracy: 0.1061 - val_loss: 2.3082 - val_accuracy: 0.1010
Epoch 18/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3055 - accuracy: 0.1037 - val_loss: 2.3059 - val_accuracy: 0.1009
Epoch 19/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3052 - accuracy: 0.1045 - val_loss: 2.3096 - val_accuracy: 0.0958
Epoch 20/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3052 - accuracy: 0.1054 - val_loss: 2.3035 - val_accuracy: 0.1028
Epoch 21/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3054 - accuracy: 0.1051 - val_loss: 2.3065 - val_accuracy: 0.1135
Epoch 22/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3048 - accuracy: 0.1081 - val_loss: 2.3052 - val_accuracy: 0.1135
Epoch 23/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3049 - accuracy: 0.1064 - val_loss: 2.3038 - val_accuracy: 0.0974
Epoch 24/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3049 - accuracy: 0.1050 - val_loss: 2.3060 - val_accuracy: 0.1028
Epoch 25/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3049 - accuracy: 0.1038 - val_loss: 2.3052 - val_accuracy: 0.1135
Epoch 26/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3045 - accuracy: 0.1087 - val_loss: 2.3053 - val_accuracy: 0.1010
Epoch 27/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3047 - accuracy: 0.1058 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 28/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3050 - accuracy: 0.1044 - val_loss: 2.3044 - val_accuracy: 0.1135
Epoch 29/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3047 - accuracy: 0.1062 - val_loss: 2.3064 - val_accuracy: 0.0980
Epoch 30/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3046 - accuracy: 0.1054 - val_loss: 2.3052 - val_accuracy: 0.0974
Epoch 31/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3046 - accuracy: 0.1064 - val_loss: 2.3053 - val_accuracy: 0.0982
Epoch 32/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3049 - accuracy: 0.1058 - val_loss: 2.3039 - val_accuracy: 0.0980
Epoch 33/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3044 - accuracy: 0.1069 - val_loss: 2.3069 - val_accuracy: 0.1028
Epoch 34/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3045 - accuracy: 0.1078 - val_loss: 2.3046 - val_accuracy: 0.1032
Epoch 35/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3045 - accuracy: 0.1059 - val_loss: 2.3059 - val_accuracy: 0.1135
Epoch 36/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3043 - accuracy: 0.1074 - val_loss: 2.3032 - val_accuracy: 0.0958
Epoch 37/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3046 - accuracy: 0.1051 - val_loss: 2.3037 - val_accuracy: 0.1028
Epoch 38/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3044 - accuracy: 0.1076 - val_loss: 2.3044 - val_accuracy: 0.1135
Epoch 39/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3043 - accuracy: 0.1069 - val_loss: 2.3055 - val_accuracy: 0.0892
Epoch 40/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3046 - accuracy: 0.1037 - val_loss: 2.3023 - val_accuracy: 0.1032
Epoch 41/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3042 - accuracy: 0.1086 - val_loss: 2.3036 - val_accuracy: 0.0982
Epoch 42/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3041 - accuracy: 0.1060 - val_loss: 2.3044 - val_accuracy: 0.1135
Epoch 43/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1073 - val_loss: 2.3053 - val_accuracy: 0.0958
Epoch 44/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3043 - accuracy: 0.1064 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 45/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3042 - accuracy: 0.1052 - val_loss: 2.3036 - val_accuracy: 0.0980
Epoch 46/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3041 - accuracy: 0.1082 - val_loss: 2.3059 - val_accuracy: 0.1135
Epoch 47/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3041 - accuracy: 0.1065 - val_loss: 2.3038 - val_accuracy: 0.1010
Epoch 48/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3037 - accuracy: 0.1086 - val_loss: 2.3029 - val_accuracy: 0.1028
Epoch 49/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3039 - accuracy: 0.1067 - val_loss: 2.3030 - val_accuracy: 0.1135
Epoch 50/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1064 - val_loss: 2.3032 - val_accuracy: 0.1010
Epoch 51/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3042 - accuracy: 0.1076 - val_loss: 2.3035 - val_accuracy: 0.1135
Epoch 52/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3039 - accuracy: 0.1070 - val_loss: 2.3019 - val_accuracy: 0.0980
Epoch 53/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3039 - accuracy: 0.1067 - val_loss: 2.3033 - val_accuracy: 0.1135
Epoch 54/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3037 - accuracy: 0.1079 - val_loss: 2.3030 - val_accuracy: 0.1135
Epoch 55/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3037 - accuracy: 0.1068 - val_loss: 2.3046 - val_accuracy: 0.1135
Epoch 56/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3039 - accuracy: 0.1077 - val_loss: 2.3030 - val_accuracy: 0.1135
Epoch 57/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1072 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 58/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1080 - val_loss: 2.3075 - val_accuracy: 0.0980
Epoch 59/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3038 - accuracy: 0.1052 - val_loss: 2.3056 - val_accuracy: 0.1010
Epoch 60/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1071 - val_loss: 2.3031 - val_accuracy: 0.1028
Epoch 61/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1067 - val_loss: 2.3043 - val_accuracy: 0.0974
Epoch 62/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3037 - accuracy: 0.1065 - val_loss: 2.3032 - val_accuracy: 0.1028
Epoch 63/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1081 - val_loss: 2.3027 - val_accuracy: 0.0980
Epoch 64/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3037 - accuracy: 0.1066 - val_loss: 2.3040 - val_accuracy: 0.1135
Epoch 65/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3035 - accuracy: 0.1071 - val_loss: 2.3032 - val_accuracy: 0.1135
Epoch 66/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1070 - val_loss: 2.3028 - val_accuracy: 0.1135
Epoch 67/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1087 - val_loss: 2.3039 - val_accuracy: 0.1135
Epoch 68/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1088 - val_loss: 2.3036 - val_accuracy: 0.1135
Epoch 69/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1063 - val_loss: 2.3023 - val_accuracy: 0.1135
Epoch 70/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1063 - val_loss: 2.3033 - val_accuracy: 0.1135
Epoch 71/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3035 - accuracy: 0.1073 - val_loss: 2.3026 - val_accuracy: 0.1010
Epoch 72/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3034 - accuracy: 0.1077 - val_loss: 2.3040 - val_accuracy: 0.1028
Epoch 73/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1073 - val_loss: 2.3045 - val_accuracy: 0.1010
Epoch 74/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1100 - val_loss: 2.3031 - val_accuracy: 0.1135
Epoch 75/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3036 - accuracy: 0.1056 - val_loss: 2.3026 - val_accuracy: 0.0980
Epoch 76/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1082 - val_loss: 2.3047 - val_accuracy: 0.0980
Epoch 77/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1075 - val_loss: 2.3028 - val_accuracy: 0.1010
Epoch 78/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3035 - accuracy: 0.1066 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 79/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3033 - accuracy: 0.1073 - val_loss: 2.3028 - val_accuracy: 0.0958
Epoch 80/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1069 - val_loss: 2.3040 - val_accuracy: 0.0958
Epoch 81/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1086 - val_loss: 2.3020 - val_accuracy: 0.1135
Epoch 82/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1079 - val_loss: 2.3029 - val_accuracy: 0.1032
Epoch 83/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1070 - val_loss: 2.3019 - val_accuracy: 0.1135
Epoch 84/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1085 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 85/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1099 - val_loss: 2.3034 - val_accuracy: 0.1135
Epoch 86/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1091 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 87/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1084 - val_loss: 2.3026 - val_accuracy: 0.1009
Epoch 88/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3033 - accuracy: 0.1070 - val_loss: 2.3029 - val_accuracy: 0.1135
Epoch 89/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1088 - val_loss: 2.3029 - val_accuracy: 0.1028
Epoch 90/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1081 - val_loss: 2.3024 - val_accuracy: 0.1032
Epoch 91/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1082 - val_loss: 2.3038 - val_accuracy: 0.0958
Epoch 92/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1075 - val_loss: 2.3031 - val_accuracy: 0.1028
Epoch 93/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1071 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 94/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1081 - val_loss: 2.3023 - val_accuracy: 0.1135
Epoch 95/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1088 - val_loss: 2.3038 - val_accuracy: 0.1135
Epoch 96/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1092 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 97/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1086 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 98/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1089 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 99/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3027 - accuracy: 0.1090 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 100/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3027 - accuracy: 0.1086 - val_loss: 2.3033 - val_accuracy: 0.1032
Epoch 101/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1071 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 102/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1083 - val_loss: 2.3024 - val_accuracy: 0.1009
Epoch 103/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3027 - accuracy: 0.1068 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 104/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3028 - accuracy: 0.1095 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 105/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3028 - accuracy: 0.1088 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 106/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1104 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 107/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1089 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 108/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1083 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 109/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1098 - val_loss: 2.3041 - val_accuracy: 0.1028
Epoch 110/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1078 - val_loss: 2.3035 - val_accuracy: 0.1135
Epoch 111/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1088 - val_loss: 2.3019 - val_accuracy: 0.1135
Epoch 112/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1100 - val_loss: 2.3035 - val_accuracy: 0.1135
Epoch 113/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1079 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 114/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3027 - accuracy: 0.1089 - val_loss: 2.3027 - val_accuracy: 0.0982
Epoch 115/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1091 - val_loss: 2.3026 - val_accuracy: 0.1009
Epoch 116/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1090 - val_loss: 2.3019 - val_accuracy: 0.1135
Epoch 117/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3025 - accuracy: 0.1092 - val_loss: 2.3033 - val_accuracy: 0.1135
Epoch 118/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3027 - accuracy: 0.1085 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 119/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3026 - accuracy: 0.1101 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 120/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1092 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 121/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1110 - val_loss: 2.3026 - val_accuracy: 0.1135
Epoch 122/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1095 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 123/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1092 - val_loss: 2.3033 - val_accuracy: 0.0980
Epoch 124/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1107 - val_loss: 2.3019 - val_accuracy: 0.1135
Epoch 125/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1099 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 126/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1093 - val_loss: 2.3024 - val_accuracy: 0.1028
Epoch 127/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1091 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 128/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1091 - val_loss: 2.3028 - val_accuracy: 0.1135
Epoch 129/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1092 - val_loss: 2.3021 - val_accuracy: 0.1135
Epoch 130/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1092 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 131/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1097 - val_loss: 2.3019 - val_accuracy: 0.1028
Epoch 132/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1078 - val_loss: 2.3021 - val_accuracy: 0.1135
Epoch 133/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1099 - val_loss: 2.3037 - val_accuracy: 0.1135
Epoch 134/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1102 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 135/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1095 - val_loss: 2.3020 - val_accuracy: 0.1135
Epoch 136/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1107 - val_loss: 2.3021 - val_accuracy: 0.1135
Epoch 137/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1120 - val_loss: 2.3026 - val_accuracy: 0.1009
Epoch 138/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1090 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 139/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1098 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 140/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1102 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 141/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1101 - val_loss: 2.3017 - val_accuracy: 0.1010
Epoch 142/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1093 - val_loss: 2.3025 - val_accuracy: 0.1135
Epoch 143/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1089 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 144/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3024 - accuracy: 0.1089 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 145/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1095 - val_loss: 2.3025 - val_accuracy: 0.1010
Epoch 146/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1103 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 147/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1102 - val_loss: 2.3022 - val_accuracy: 0.1135
Epoch 148/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1102 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 149/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1105 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 150/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1111 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 151/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1093 - val_loss: 2.3023 - val_accuracy: 0.1009
Epoch 152/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1086 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 153/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1106 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 154/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1110 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 155/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1107 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 156/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1105 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 157/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1099 - val_loss: 2.3032 - val_accuracy: 0.1028
Epoch 158/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1114 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 159/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1099 - val_loss: 2.3027 - val_accuracy: 0.1028
Epoch 160/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1096 - val_loss: 2.3023 - val_accuracy: 0.1135
Epoch 161/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1101 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 162/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1118 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 163/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3023 - accuracy: 0.1110 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 164/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1103 - val_loss: 2.3013 - val_accuracy: 0.1135
Epoch 165/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1097 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 166/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1106 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 167/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1111 - val_loss: 2.3017 - val_accuracy: 0.1028
Epoch 168/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1109 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 169/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1108 - val_loss: 2.3011 - val_accuracy: 0.1135
Epoch 170/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1105 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 171/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1103 - val_loss: 2.3023 - val_accuracy: 0.1135
Epoch 172/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1102 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 173/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1097 - val_loss: 2.3017 - val_accuracy: 0.1135
Epoch 174/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1103 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 175/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1117 - val_loss: 2.3023 - val_accuracy: 0.1135
Epoch 176/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1111 - val_loss: 2.3015 - val_accuracy: 0.1028
Epoch 177/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1102 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 178/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1101 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 179/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1115 - val_loss: 2.3012 - val_accuracy: 0.1135
Epoch 180/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1110 - val_loss: 2.3012 - val_accuracy: 0.1135
Epoch 181/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1114 - val_loss: 2.3015 - val_accuracy: 0.1028
Epoch 182/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1101 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 183/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1113 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 184/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1103 - val_loss: 2.3012 - val_accuracy: 0.1135
Epoch 185/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1106 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 186/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1107 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 187/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1114 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 188/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1112 - val_loss: 2.3019 - val_accuracy: 0.1010
Epoch 189/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3021 - accuracy: 0.1101 - val_loss: 2.3014 - val_accuracy: 0.1135
Epoch 190/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1113 - val_loss: 2.3013 - val_accuracy: 0.1135
Epoch 191/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1098 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 192/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3022 - accuracy: 0.1111 - val_loss: 2.3018 - val_accuracy: 0.1135
Epoch 193/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1111 - val_loss: 2.3012 - val_accuracy: 0.1135
Epoch 194/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3018 - accuracy: 0.1113 - val_loss: 2.3022 - val_accuracy: 0.1028
Epoch 195/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3018 - accuracy: 0.1106 - val_loss: 2.3020 - val_accuracy: 0.1028
Epoch 196/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1105 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 197/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1102 - val_loss: 2.3013 - val_accuracy: 0.1135
Epoch 198/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3020 - accuracy: 0.1114 - val_loss: 2.3017 - val_accuracy: 0.1028
Epoch 199/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1107 - val_loss: 2.3016 - val_accuracy: 0.1135
Epoch 200/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3019 - accuracy: 0.1113 - val_loss: 2.3017 - val_accuracy: 0.1135

(b) Activation function: the logistic sigmoid function; initialization: Xavier initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_9 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_54 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_55 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_56 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_57 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_58 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_59 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.SGD(learning_rate=.01)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
history1 = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3078 - accuracy: 0.1043 - val_loss: 2.3109 - val_accuracy: 0.1135
Epoch 2/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3065 - accuracy: 0.1063 - val_loss: 2.3083 - val_accuracy: 0.1010
Epoch 3/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3066 - accuracy: 0.1057 - val_loss: 2.3127 - val_accuracy: 0.1010
Epoch 4/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3067 - accuracy: 0.1056 - val_loss: 2.3029 - val_accuracy: 0.0980
Epoch 5/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3066 - accuracy: 0.1043 - val_loss: 2.3063 - val_accuracy: 0.0958
Epoch 6/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3063 - accuracy: 0.1063 - val_loss: 2.3069 - val_accuracy: 0.1028
Epoch 7/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3062 - accuracy: 0.1041 - val_loss: 2.3083 - val_accuracy: 0.1010
Epoch 8/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3061 - accuracy: 0.1036 - val_loss: 2.3062 - val_accuracy: 0.1135
Epoch 9/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3056 - accuracy: 0.1057 - val_loss: 2.3031 - val_accuracy: 0.0982
Epoch 10/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3060 - accuracy: 0.1056 - val_loss: 2.3060 - val_accuracy: 0.1135
Epoch 11/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3056 - accuracy: 0.1049 - val_loss: 2.3063 - val_accuracy: 0.0958
Epoch 12/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3057 - accuracy: 0.1062 - val_loss: 2.3097 - val_accuracy: 0.1009
Epoch 13/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3055 - accuracy: 0.1071 - val_loss: 2.3076 - val_accuracy: 0.1032
Epoch 14/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3054 - accuracy: 0.1047 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 15/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3056 - accuracy: 0.1054 - val_loss: 2.3071 - val_accuracy: 0.1028
Epoch 16/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3050 - accuracy: 0.1077 - val_loss: 2.3015 - val_accuracy: 0.1135
Epoch 17/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3054 - accuracy: 0.1076 - val_loss: 2.3100 - val_accuracy: 0.1028
Epoch 18/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3048 - accuracy: 0.1055 - val_loss: 2.3055 - val_accuracy: 0.1862
Epoch 19/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3050 - accuracy: 0.1046 - val_loss: 2.3042 - val_accuracy: 0.1135
Epoch 20/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3045 - accuracy: 0.1088 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 21/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3046 - accuracy: 0.1057 - val_loss: 2.3041 - val_accuracy: 0.1032
Epoch 22/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3043 - accuracy: 0.1084 - val_loss: 2.3047 - val_accuracy: 0.1135
Epoch 23/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1079 - val_loss: 2.3143 - val_accuracy: 0.0974
Epoch 24/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3042 - accuracy: 0.1086 - val_loss: 2.3024 - val_accuracy: 0.1135
Epoch 25/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1067 - val_loss: 2.3039 - val_accuracy: 0.1135
Epoch 26/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3038 - accuracy: 0.1090 - val_loss: 2.3009 - val_accuracy: 0.1032
Epoch 27/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3040 - accuracy: 0.1088 - val_loss: 2.3058 - val_accuracy: 0.1135
Epoch 28/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1098 - val_loss: 2.3066 - val_accuracy: 0.1028
Epoch 29/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3032 - accuracy: 0.1109 - val_loss: 2.3050 - val_accuracy: 0.1135
Epoch 30/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3033 - accuracy: 0.1070 - val_loss: 2.3055 - val_accuracy: 0.1028
Epoch 31/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3031 - accuracy: 0.1101 - val_loss: 2.3072 - val_accuracy: 0.1028
Epoch 32/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3029 - accuracy: 0.1086 - val_loss: 2.3042 - val_accuracy: 0.1010
Epoch 33/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3030 - accuracy: 0.1120 - val_loss: 2.3060 - val_accuracy: 0.1028
Epoch 34/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1099 - val_loss: 2.3001 - val_accuracy: 0.1135
Epoch 35/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3026 - accuracy: 0.1111 - val_loss: 2.3060 - val_accuracy: 0.1135
Epoch 36/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3025 - accuracy: 0.1119 - val_loss: 2.2992 - val_accuracy: 0.1135
Epoch 37/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3018 - accuracy: 0.1126 - val_loss: 2.3028 - val_accuracy: 0.2090
Epoch 38/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3015 - accuracy: 0.1121 - val_loss: 2.3025 - val_accuracy: 0.0958
Epoch 39/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3013 - accuracy: 0.1155 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 40/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3009 - accuracy: 0.1172 - val_loss: 2.3027 - val_accuracy: 0.1135
Epoch 41/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3004 - accuracy: 0.1160 - val_loss: 2.3000 - val_accuracy: 0.1197
Epoch 42/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2997 - accuracy: 0.1182 - val_loss: 2.3017 - val_accuracy: 0.0958
Epoch 43/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2996 - accuracy: 0.1152 - val_loss: 2.3019 - val_accuracy: 0.0958
Epoch 44/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2995 - accuracy: 0.1187 - val_loss: 2.3006 - val_accuracy: 0.1010
Epoch 45/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2988 - accuracy: 0.1181 - val_loss: 2.2976 - val_accuracy: 0.1135
Epoch 46/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2979 - accuracy: 0.1249 - val_loss: 2.2972 - val_accuracy: 0.1135
Epoch 47/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2971 - accuracy: 0.1240 - val_loss: 2.2986 - val_accuracy: 0.1030
Epoch 48/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2958 - accuracy: 0.1275 - val_loss: 2.2934 - val_accuracy: 0.1135
Epoch 49/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2946 - accuracy: 0.1323 - val_loss: 2.2968 - val_accuracy: 0.1634
Epoch 50/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2930 - accuracy: 0.1366 - val_loss: 2.2911 - val_accuracy: 0.2009
Epoch 51/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2905 - accuracy: 0.1453 - val_loss: 2.2896 - val_accuracy: 0.1032
Epoch 52/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2876 - accuracy: 0.1502 - val_loss: 2.2825 - val_accuracy: 0.1471
Epoch 53/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2826 - accuracy: 0.1672 - val_loss: 2.2802 - val_accuracy: 0.1670
Epoch 54/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2749 - accuracy: 0.1927 - val_loss: 2.2700 - val_accuracy: 0.1900
Epoch 55/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2605 - accuracy: 0.2166 - val_loss: 2.2563 - val_accuracy: 0.1463
Epoch 56/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2296 - accuracy: 0.2388 - val_loss: 2.2019 - val_accuracy: 0.2626
Epoch 57/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1527 - accuracy: 0.2533 - val_loss: 2.0813 - val_accuracy: 0.2496
Epoch 58/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9955 - accuracy: 0.2742 - val_loss: 1.9011 - val_accuracy: 0.2962
Epoch 59/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8542 - accuracy: 0.3022 - val_loss: 1.7988 - val_accuracy: 0.3156
Epoch 60/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7794 - accuracy: 0.3248 - val_loss: 1.7413 - val_accuracy: 0.3226
Epoch 61/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7257 - accuracy: 0.3378 - val_loss: 1.6775 - val_accuracy: 0.3694
Epoch 62/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6731 - accuracy: 0.3541 - val_loss: 1.6257 - val_accuracy: 0.3679
Epoch 63/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6298 - accuracy: 0.3687 - val_loss: 1.5857 - val_accuracy: 0.3675
Epoch 64/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6003 - accuracy: 0.3732 - val_loss: 1.5648 - val_accuracy: 0.3718
Epoch 65/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5805 - accuracy: 0.3830 - val_loss: 1.5520 - val_accuracy: 0.3790
Epoch 66/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5661 - accuracy: 0.3851 - val_loss: 1.5336 - val_accuracy: 0.3942
Epoch 67/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5546 - accuracy: 0.3906 - val_loss: 1.5315 - val_accuracy: 0.3868
Epoch 68/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5430 - accuracy: 0.3971 - val_loss: 1.5151 - val_accuracy: 0.4122
Epoch 69/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5323 - accuracy: 0.4053 - val_loss: 1.5072 - val_accuracy: 0.3905
Epoch 70/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5216 - accuracy: 0.4084 - val_loss: 1.4968 - val_accuracy: 0.4075
Epoch 71/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5088 - accuracy: 0.4168 - val_loss: 1.4868 - val_accuracy: 0.4135
Epoch 72/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4956 - accuracy: 0.4264 - val_loss: 1.4746 - val_accuracy: 0.4447
Epoch 73/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4807 - accuracy: 0.4370 - val_loss: 1.4494 - val_accuracy: 0.4352
Epoch 74/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4633 - accuracy: 0.4509 - val_loss: 1.4287 - val_accuracy: 0.4682
Epoch 75/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4409 - accuracy: 0.4690 - val_loss: 1.4030 - val_accuracy: 0.4987
Epoch 76/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4085 - accuracy: 0.4946 - val_loss: 1.3632 - val_accuracy: 0.5318
Epoch 77/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3549 - accuracy: 0.5363 - val_loss: 1.2959 - val_accuracy: 0.5382
Epoch 78/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2619 - accuracy: 0.5797 - val_loss: 1.1798 - val_accuracy: 0.6005
Epoch 79/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1458 - accuracy: 0.6191 - val_loss: 1.0769 - val_accuracy: 0.6412
Epoch 80/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0613 - accuracy: 0.6472 - val_loss: 1.0075 - val_accuracy: 0.6533
Epoch 81/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0103 - accuracy: 0.6638 - val_loss: 0.9783 - val_accuracy: 0.6616
Epoch 82/200
938/938 [==============================] - 3s 3ms/step - loss: 0.9736 - accuracy: 0.6785 - val_loss: 0.9476 - val_accuracy: 0.6707
Epoch 83/200
938/938 [==============================] - 3s 3ms/step - loss: 0.9405 - accuracy: 0.6907 - val_loss: 0.9224 - val_accuracy: 0.6842
Epoch 84/200
938/938 [==============================] - 3s 3ms/step - loss: 0.9068 - accuracy: 0.7056 - val_loss: 0.8669 - val_accuracy: 0.7058
Epoch 85/200
938/938 [==============================] - 3s 3ms/step - loss: 0.8686 - accuracy: 0.7210 - val_loss: 0.8313 - val_accuracy: 0.7229
Epoch 86/200
938/938 [==============================] - 3s 3ms/step - loss: 0.8234 - accuracy: 0.7376 - val_loss: 0.7844 - val_accuracy: 0.7443
Epoch 87/200
938/938 [==============================] - 3s 3ms/step - loss: 0.7732 - accuracy: 0.7566 - val_loss: 0.7335 - val_accuracy: 0.7673
Epoch 88/200
938/938 [==============================] - 3s 3ms/step - loss: 0.7268 - accuracy: 0.7723 - val_loss: 0.7055 - val_accuracy: 0.7721
Epoch 89/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6904 - accuracy: 0.7830 - val_loss: 0.6622 - val_accuracy: 0.7926
Epoch 90/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6636 - accuracy: 0.7938 - val_loss: 0.6338 - val_accuracy: 0.8043
Epoch 91/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6422 - accuracy: 0.8009 - val_loss: 0.6340 - val_accuracy: 0.7997
Epoch 92/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6244 - accuracy: 0.8076 - val_loss: 0.6242 - val_accuracy: 0.7975
Epoch 93/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6093 - accuracy: 0.8151 - val_loss: 0.5909 - val_accuracy: 0.8207
Epoch 94/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5950 - accuracy: 0.8202 - val_loss: 0.5760 - val_accuracy: 0.8255
Epoch 95/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5828 - accuracy: 0.8255 - val_loss: 0.5644 - val_accuracy: 0.8318
Epoch 96/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5703 - accuracy: 0.8304 - val_loss: 0.5515 - val_accuracy: 0.8367
Epoch 97/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5592 - accuracy: 0.8349 - val_loss: 0.5392 - val_accuracy: 0.8405
Epoch 98/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5491 - accuracy: 0.8394 - val_loss: 0.5353 - val_accuracy: 0.8445
Epoch 99/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5397 - accuracy: 0.8419 - val_loss: 0.5231 - val_accuracy: 0.8466
Epoch 100/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5297 - accuracy: 0.8458 - val_loss: 0.5143 - val_accuracy: 0.8530
Epoch 101/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5214 - accuracy: 0.8497 - val_loss: 0.5245 - val_accuracy: 0.8493
Epoch 102/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5136 - accuracy: 0.8531 - val_loss: 0.5037 - val_accuracy: 0.8539
Epoch 103/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5059 - accuracy: 0.8557 - val_loss: 0.4947 - val_accuracy: 0.8571
Epoch 104/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4977 - accuracy: 0.8586 - val_loss: 0.4852 - val_accuracy: 0.8637
Epoch 105/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4913 - accuracy: 0.8613 - val_loss: 0.4754 - val_accuracy: 0.8682
Epoch 106/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4850 - accuracy: 0.8640 - val_loss: 0.4748 - val_accuracy: 0.8704
Epoch 107/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4781 - accuracy: 0.8665 - val_loss: 0.4700 - val_accuracy: 0.8705
Epoch 108/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4719 - accuracy: 0.8679 - val_loss: 0.4809 - val_accuracy: 0.8647
Epoch 109/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4657 - accuracy: 0.8705 - val_loss: 0.4564 - val_accuracy: 0.8752
Epoch 110/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4600 - accuracy: 0.8723 - val_loss: 0.4489 - val_accuracy: 0.8786
Epoch 111/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4549 - accuracy: 0.8738 - val_loss: 0.4701 - val_accuracy: 0.8702
Epoch 112/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4486 - accuracy: 0.8762 - val_loss: 0.4378 - val_accuracy: 0.8817
Epoch 113/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4428 - accuracy: 0.8782 - val_loss: 0.4334 - val_accuracy: 0.8829
Epoch 114/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4370 - accuracy: 0.8805 - val_loss: 0.4373 - val_accuracy: 0.8796
Epoch 115/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4326 - accuracy: 0.8811 - val_loss: 0.4236 - val_accuracy: 0.8865
Epoch 116/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4274 - accuracy: 0.8837 - val_loss: 0.4328 - val_accuracy: 0.8840
Epoch 117/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4224 - accuracy: 0.8851 - val_loss: 0.4269 - val_accuracy: 0.8833
Epoch 118/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4174 - accuracy: 0.8873 - val_loss: 0.4113 - val_accuracy: 0.8915
Epoch 119/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4122 - accuracy: 0.8884 - val_loss: 0.4101 - val_accuracy: 0.8896
Epoch 120/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4077 - accuracy: 0.8899 - val_loss: 0.4069 - val_accuracy: 0.8916
Epoch 121/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4035 - accuracy: 0.8911 - val_loss: 0.4194 - val_accuracy: 0.8853
Epoch 122/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3986 - accuracy: 0.8924 - val_loss: 0.3988 - val_accuracy: 0.8939
Epoch 123/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3946 - accuracy: 0.8935 - val_loss: 0.4163 - val_accuracy: 0.8858
Epoch 124/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3895 - accuracy: 0.8949 - val_loss: 0.3929 - val_accuracy: 0.8972
Epoch 125/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3846 - accuracy: 0.8968 - val_loss: 0.3888 - val_accuracy: 0.8982
Epoch 126/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3808 - accuracy: 0.8977 - val_loss: 0.4029 - val_accuracy: 0.8915
Epoch 127/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3759 - accuracy: 0.8989 - val_loss: 0.3966 - val_accuracy: 0.8919
Epoch 128/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3723 - accuracy: 0.8994 - val_loss: 0.3925 - val_accuracy: 0.8953
Epoch 129/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3679 - accuracy: 0.9016 - val_loss: 0.3693 - val_accuracy: 0.9032
Epoch 130/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3632 - accuracy: 0.9031 - val_loss: 0.3832 - val_accuracy: 0.8978
Epoch 131/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3598 - accuracy: 0.9041 - val_loss: 0.3869 - val_accuracy: 0.8965
Epoch 132/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3557 - accuracy: 0.9049 - val_loss: 0.3652 - val_accuracy: 0.9063
Epoch 133/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3509 - accuracy: 0.9069 - val_loss: 0.3900 - val_accuracy: 0.8952
Epoch 134/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3479 - accuracy: 0.9076 - val_loss: 0.3610 - val_accuracy: 0.9069
Epoch 135/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3433 - accuracy: 0.9093 - val_loss: 0.3489 - val_accuracy: 0.9107
Epoch 136/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3400 - accuracy: 0.9094 - val_loss: 0.3513 - val_accuracy: 0.9097
Epoch 137/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3356 - accuracy: 0.9115 - val_loss: 0.3502 - val_accuracy: 0.9098
Epoch 138/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3326 - accuracy: 0.9129 - val_loss: 0.3407 - val_accuracy: 0.9136
Epoch 139/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3288 - accuracy: 0.9134 - val_loss: 0.3536 - val_accuracy: 0.9069
Epoch 140/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3232 - accuracy: 0.9154 - val_loss: 0.3444 - val_accuracy: 0.9112
Epoch 141/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3212 - accuracy: 0.9161 - val_loss: 0.3433 - val_accuracy: 0.9108
Epoch 142/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3179 - accuracy: 0.9166 - val_loss: 0.3308 - val_accuracy: 0.9177
Epoch 143/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3137 - accuracy: 0.9175 - val_loss: 0.3549 - val_accuracy: 0.9066
Epoch 144/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3104 - accuracy: 0.9185 - val_loss: 0.3260 - val_accuracy: 0.9178
Epoch 145/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3057 - accuracy: 0.9198 - val_loss: 0.3258 - val_accuracy: 0.9156
Epoch 146/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3030 - accuracy: 0.9194 - val_loss: 0.3197 - val_accuracy: 0.9185
Epoch 147/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2989 - accuracy: 0.9215 - val_loss: 0.3427 - val_accuracy: 0.9110
Epoch 148/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2956 - accuracy: 0.9221 - val_loss: 0.3324 - val_accuracy: 0.9125
Epoch 149/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2921 - accuracy: 0.9239 - val_loss: 0.3194 - val_accuracy: 0.9185
Epoch 150/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2884 - accuracy: 0.9237 - val_loss: 0.3103 - val_accuracy: 0.9199
Epoch 151/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2849 - accuracy: 0.9253 - val_loss: 0.3444 - val_accuracy: 0.9085
Epoch 152/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2813 - accuracy: 0.9261 - val_loss: 0.3023 - val_accuracy: 0.9224
Epoch 153/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2791 - accuracy: 0.9266 - val_loss: 0.3059 - val_accuracy: 0.9220
Epoch 154/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2754 - accuracy: 0.9270 - val_loss: 0.3060 - val_accuracy: 0.9196
Epoch 155/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2702 - accuracy: 0.9283 - val_loss: 0.2915 - val_accuracy: 0.9247
Epoch 156/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2682 - accuracy: 0.9288 - val_loss: 0.2869 - val_accuracy: 0.9270
Epoch 157/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2636 - accuracy: 0.9307 - val_loss: 0.2838 - val_accuracy: 0.9271
Epoch 158/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2601 - accuracy: 0.9314 - val_loss: 0.2892 - val_accuracy: 0.9259
Epoch 159/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2575 - accuracy: 0.9320 - val_loss: 0.2869 - val_accuracy: 0.9264
Epoch 160/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2558 - accuracy: 0.9329 - val_loss: 0.2837 - val_accuracy: 0.9280
Epoch 161/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2505 - accuracy: 0.9336 - val_loss: 0.2856 - val_accuracy: 0.9248
Epoch 162/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2479 - accuracy: 0.9340 - val_loss: 0.2721 - val_accuracy: 0.9287
Epoch 163/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2451 - accuracy: 0.9350 - val_loss: 0.2701 - val_accuracy: 0.9303
Epoch 164/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2420 - accuracy: 0.9364 - val_loss: 0.2861 - val_accuracy: 0.9244
Epoch 165/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2388 - accuracy: 0.9362 - val_loss: 0.3065 - val_accuracy: 0.9186
Epoch 166/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2356 - accuracy: 0.9377 - val_loss: 0.2721 - val_accuracy: 0.9285
Epoch 167/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2323 - accuracy: 0.9389 - val_loss: 0.2758 - val_accuracy: 0.9277
Epoch 168/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2285 - accuracy: 0.9392 - val_loss: 0.2881 - val_accuracy: 0.9227
Epoch 169/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2257 - accuracy: 0.9406 - val_loss: 0.2537 - val_accuracy: 0.9350
Epoch 170/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2238 - accuracy: 0.9406 - val_loss: 0.2580 - val_accuracy: 0.9321
Epoch 171/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2198 - accuracy: 0.9415 - val_loss: 0.2459 - val_accuracy: 0.9356
Epoch 172/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2172 - accuracy: 0.9417 - val_loss: 0.2496 - val_accuracy: 0.9356
Epoch 173/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2142 - accuracy: 0.9432 - val_loss: 0.2442 - val_accuracy: 0.9357
Epoch 174/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2123 - accuracy: 0.9436 - val_loss: 0.2440 - val_accuracy: 0.9355
Epoch 175/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2082 - accuracy: 0.9446 - val_loss: 0.2395 - val_accuracy: 0.9357
Epoch 176/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2059 - accuracy: 0.9456 - val_loss: 0.2414 - val_accuracy: 0.9361
Epoch 177/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2038 - accuracy: 0.9456 - val_loss: 0.2298 - val_accuracy: 0.9388
Epoch 178/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2008 - accuracy: 0.9465 - val_loss: 0.2290 - val_accuracy: 0.9396
Epoch 179/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1983 - accuracy: 0.9470 - val_loss: 0.2399 - val_accuracy: 0.9366
Epoch 180/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1956 - accuracy: 0.9477 - val_loss: 0.2264 - val_accuracy: 0.9401
Epoch 181/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1917 - accuracy: 0.9485 - val_loss: 0.2225 - val_accuracy: 0.9406
Epoch 182/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1902 - accuracy: 0.9494 - val_loss: 0.2237 - val_accuracy: 0.9400
Epoch 183/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1868 - accuracy: 0.9506 - val_loss: 0.2181 - val_accuracy: 0.9420
Epoch 184/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1843 - accuracy: 0.9506 - val_loss: 0.2160 - val_accuracy: 0.9422
Epoch 185/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1821 - accuracy: 0.9512 - val_loss: 0.2597 - val_accuracy: 0.9315
Epoch 186/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1795 - accuracy: 0.9521 - val_loss: 0.2176 - val_accuracy: 0.9431
Epoch 187/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1781 - accuracy: 0.9521 - val_loss: 0.2089 - val_accuracy: 0.9449
Epoch 188/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1750 - accuracy: 0.9529 - val_loss: 0.2172 - val_accuracy: 0.9392
Epoch 189/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1733 - accuracy: 0.9531 - val_loss: 0.2328 - val_accuracy: 0.9391
Epoch 190/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1713 - accuracy: 0.9540 - val_loss: 0.2079 - val_accuracy: 0.9446
Epoch 191/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1690 - accuracy: 0.9548 - val_loss: 0.2239 - val_accuracy: 0.9417
Epoch 192/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1680 - accuracy: 0.9545 - val_loss: 0.2346 - val_accuracy: 0.9357
Epoch 193/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1647 - accuracy: 0.9547 - val_loss: 0.2040 - val_accuracy: 0.9447
Epoch 194/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1621 - accuracy: 0.9561 - val_loss: 0.2155 - val_accuracy: 0.9436
Epoch 195/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1604 - accuracy: 0.9564 - val_loss: 0.1947 - val_accuracy: 0.9480
Epoch 196/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1579 - accuracy: 0.9571 - val_loss: 0.1982 - val_accuracy: 0.9480
Epoch 197/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1565 - accuracy: 0.9575 - val_loss: 0.2075 - val_accuracy: 0.9449
Epoch 198/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1543 - accuracy: 0.9581 - val_loss: 0.1888 - val_accuracy: 0.9484
Epoch 199/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1515 - accuracy: 0.9585 - val_loss: 0.1892 - val_accuracy: 0.9480
Epoch 200/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1503 - accuracy: 0.9587 - val_loss: 0.1925 - val_accuracy: 0.9485

(c) Activation function: ReLU; initialization: random numbers generated from the normal

distribution (μ = 0, σ = 0.01)

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_10"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_10 (Flatten)         (None, 784)               0         
_________________________________________________________________
dense_60 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_61 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_62 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_63 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_64 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_65 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.SGD(learning_rate=.01)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
history2 = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3018 - accuracy: 0.1120 - val_loss: 2.3013 - val_accuracy: 0.1135
Epoch 2/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3013 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 3/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 4/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 5/200
938/938 [==============================] - 3s 4ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 6/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 7/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 8/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 9/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 10/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 11/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 12/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 13/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 14/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 15/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 16/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 17/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 18/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 19/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 20/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 21/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 22/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 23/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 24/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 25/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 26/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 27/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3012 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 28/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 29/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 30/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3010 - val_accuracy: 0.1135
Epoch 31/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 32/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 33/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 34/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 35/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 36/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 37/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 38/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3009 - val_accuracy: 0.1135
Epoch 39/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 40/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3011 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 41/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3010 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 42/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3010 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 43/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3010 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 44/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3010 - accuracy: 0.1124 - val_loss: 2.3008 - val_accuracy: 0.1135
Epoch 45/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3010 - accuracy: 0.1124 - val_loss: 2.3007 - val_accuracy: 0.1135
Epoch 46/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3009 - accuracy: 0.1124 - val_loss: 2.3007 - val_accuracy: 0.1135
Epoch 47/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3009 - accuracy: 0.1124 - val_loss: 2.3007 - val_accuracy: 0.1135
Epoch 48/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3008 - accuracy: 0.1124 - val_loss: 2.3006 - val_accuracy: 0.1135
Epoch 49/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3008 - accuracy: 0.1124 - val_loss: 2.3005 - val_accuracy: 0.1135
Epoch 50/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3007 - accuracy: 0.1124 - val_loss: 2.3004 - val_accuracy: 0.1135
Epoch 51/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3006 - accuracy: 0.1124 - val_loss: 2.3003 - val_accuracy: 0.1135
Epoch 52/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3004 - accuracy: 0.1124 - val_loss: 2.3001 - val_accuracy: 0.1135
Epoch 53/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3001 - accuracy: 0.1124 - val_loss: 2.2997 - val_accuracy: 0.1135
Epoch 54/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2997 - accuracy: 0.1124 - val_loss: 2.2991 - val_accuracy: 0.1135
Epoch 55/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2987 - accuracy: 0.1124 - val_loss: 2.2975 - val_accuracy: 0.1135
Epoch 56/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2959 - accuracy: 0.1124 - val_loss: 2.2922 - val_accuracy: 0.1135
Epoch 57/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2766 - accuracy: 0.1768 - val_loss: 2.2261 - val_accuracy: 0.2105
Epoch 58/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0037 - accuracy: 0.2184 - val_loss: 1.8252 - val_accuracy: 0.2414
Epoch 59/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7324 - accuracy: 0.2667 - val_loss: 1.6546 - val_accuracy: 0.3045
Epoch 60/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5745 - accuracy: 0.3407 - val_loss: 1.4671 - val_accuracy: 0.4449
Epoch 61/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3193 - accuracy: 0.4944 - val_loss: 1.1430 - val_accuracy: 0.5548
Epoch 62/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0701 - accuracy: 0.5969 - val_loss: 1.0867 - val_accuracy: 0.5596
Epoch 63/200
938/938 [==============================] - 3s 3ms/step - loss: 0.9064 - accuracy: 0.6730 - val_loss: 1.0725 - val_accuracy: 0.5881
Epoch 64/200
938/938 [==============================] - 3s 3ms/step - loss: 0.7820 - accuracy: 0.7400 - val_loss: 0.9289 - val_accuracy: 0.6774
Epoch 65/200
938/938 [==============================] - 3s 3ms/step - loss: 0.6609 - accuracy: 0.8132 - val_loss: 0.6117 - val_accuracy: 0.8500
Epoch 66/200
938/938 [==============================] - 3s 3ms/step - loss: 0.5523 - accuracy: 0.8520 - val_loss: 0.5201 - val_accuracy: 0.8765
Epoch 67/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4572 - accuracy: 0.8789 - val_loss: 0.6081 - val_accuracy: 0.8021
Epoch 68/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3870 - accuracy: 0.8978 - val_loss: 0.4010 - val_accuracy: 0.9048
Epoch 69/200
938/938 [==============================] - 3s 3ms/step - loss: 0.3313 - accuracy: 0.9132 - val_loss: 0.4584 - val_accuracy: 0.8751
Epoch 70/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2853 - accuracy: 0.9247 - val_loss: 0.3507 - val_accuracy: 0.9173
Epoch 71/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2494 - accuracy: 0.9324 - val_loss: 0.3447 - val_accuracy: 0.9184
Epoch 72/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2188 - accuracy: 0.9404 - val_loss: 0.4062 - val_accuracy: 0.8928
Epoch 73/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1907 - accuracy: 0.9481 - val_loss: 0.3564 - val_accuracy: 0.9190
Epoch 74/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1693 - accuracy: 0.9533 - val_loss: 0.3268 - val_accuracy: 0.9289
Epoch 75/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1478 - accuracy: 0.9589 - val_loss: 0.3117 - val_accuracy: 0.9292
Epoch 76/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1310 - accuracy: 0.9632 - val_loss: 0.3025 - val_accuracy: 0.9323
Epoch 77/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1156 - accuracy: 0.9685 - val_loss: 0.3100 - val_accuracy: 0.9362
Epoch 78/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1004 - accuracy: 0.9727 - val_loss: 0.3141 - val_accuracy: 0.9379
Epoch 79/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0888 - accuracy: 0.9756 - val_loss: 0.3371 - val_accuracy: 0.9249
Epoch 80/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0793 - accuracy: 0.9786 - val_loss: 0.3176 - val_accuracy: 0.9401
Epoch 81/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0709 - accuracy: 0.9806 - val_loss: 0.3252 - val_accuracy: 0.9358
Epoch 82/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0677 - accuracy: 0.9811 - val_loss: 0.3060 - val_accuracy: 0.9412
Epoch 83/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0575 - accuracy: 0.9839 - val_loss: 0.3431 - val_accuracy: 0.9379
Epoch 84/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0504 - accuracy: 0.9859 - val_loss: 0.3831 - val_accuracy: 0.9302
Epoch 85/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0473 - accuracy: 0.9870 - val_loss: 0.3190 - val_accuracy: 0.9455
Epoch 86/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0445 - accuracy: 0.9875 - val_loss: 0.3115 - val_accuracy: 0.9443
Epoch 87/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0339 - accuracy: 0.9909 - val_loss: 0.3321 - val_accuracy: 0.9461
Epoch 88/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0296 - accuracy: 0.9923 - val_loss: 0.3446 - val_accuracy: 0.9438
Epoch 89/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0278 - accuracy: 0.9926 - val_loss: 0.3556 - val_accuracy: 0.9460
Epoch 90/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0280 - accuracy: 0.9922 - val_loss: 0.4406 - val_accuracy: 0.9300
Epoch 91/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0254 - accuracy: 0.9931 - val_loss: 0.3440 - val_accuracy: 0.9483
Epoch 92/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0198 - accuracy: 0.9948 - val_loss: 0.3568 - val_accuracy: 0.9452
Epoch 93/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0140 - accuracy: 0.9970 - val_loss: 0.3722 - val_accuracy: 0.9478
Epoch 94/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0123 - accuracy: 0.9974 - val_loss: 0.5255 - val_accuracy: 0.9292
Epoch 95/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0107 - accuracy: 0.9979 - val_loss: 0.3867 - val_accuracy: 0.9455
Epoch 96/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0090 - accuracy: 0.9981 - val_loss: 0.3762 - val_accuracy: 0.9485
Epoch 97/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0088 - accuracy: 0.9982 - val_loss: 0.3843 - val_accuracy: 0.9496
Epoch 98/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0078 - accuracy: 0.9986 - val_loss: 0.3736 - val_accuracy: 0.9503
Epoch 99/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0060 - accuracy: 0.9990 - val_loss: 0.3947 - val_accuracy: 0.9496
Epoch 100/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0059 - accuracy: 0.9988 - val_loss: 0.5080 - val_accuracy: 0.9187
Epoch 101/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0056 - accuracy: 0.9991 - val_loss: 0.4122 - val_accuracy: 0.9478
Epoch 102/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0046 - accuracy: 0.9994 - val_loss: 0.4045 - val_accuracy: 0.9508
Epoch 103/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0042 - accuracy: 0.9994 - val_loss: 0.4263 - val_accuracy: 0.9484
Epoch 104/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.4234 - val_accuracy: 0.9496
Epoch 105/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0031 - accuracy: 0.9997 - val_loss: 0.4072 - val_accuracy: 0.9511
Epoch 106/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0030 - accuracy: 0.9997 - val_loss: 0.4200 - val_accuracy: 0.9509
Epoch 107/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.4367 - val_accuracy: 0.9518
Epoch 108/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.4335 - val_accuracy: 0.9503
Epoch 109/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.4289 - val_accuracy: 0.9496
Epoch 110/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0088 - accuracy: 0.9980 - val_loss: 0.5239 - val_accuracy: 0.9338
Epoch 111/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0294 - accuracy: 0.9905 - val_loss: 0.4105 - val_accuracy: 0.9459
Epoch 112/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0308 - accuracy: 0.9903 - val_loss: 0.4146 - val_accuracy: 0.9498
Epoch 113/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0235 - accuracy: 0.9923 - val_loss: 0.3854 - val_accuracy: 0.9507
Epoch 114/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0152 - accuracy: 0.9956 - val_loss: 0.5354 - val_accuracy: 0.9352
Epoch 115/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0096 - accuracy: 0.9974 - val_loss: 0.3948 - val_accuracy: 0.9517
Epoch 116/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 0.3994 - val_accuracy: 0.9534
Epoch 117/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0021 - accuracy: 0.9998 - val_loss: 0.4176 - val_accuracy: 0.9529
Epoch 118/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.4091 - val_accuracy: 0.9519
Epoch 119/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.4275 - val_accuracy: 0.9522
Epoch 120/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.4227 - val_accuracy: 0.9533
Epoch 121/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.4319 - val_accuracy: 0.9532
Epoch 122/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.4293 - val_accuracy: 0.9524
Epoch 123/200
938/938 [==============================] - 3s 3ms/step - loss: 7.2665e-04 - accuracy: 0.9999 - val_loss: 0.4409 - val_accuracy: 0.9524
Epoch 124/200
938/938 [==============================] - 3s 3ms/step - loss: 6.3522e-04 - accuracy: 1.0000 - val_loss: 0.4413 - val_accuracy: 0.9526
Epoch 125/200
938/938 [==============================] - 3s 3ms/step - loss: 5.0149e-04 - accuracy: 1.0000 - val_loss: 0.4458 - val_accuracy: 0.9527
Epoch 126/200
938/938 [==============================] - 3s 3ms/step - loss: 4.7013e-04 - accuracy: 1.0000 - val_loss: 0.4517 - val_accuracy: 0.9526
Epoch 127/200
938/938 [==============================] - 3s 3ms/step - loss: 4.3521e-04 - accuracy: 1.0000 - val_loss: 0.4608 - val_accuracy: 0.9521
Epoch 128/200
938/938 [==============================] - 3s 3ms/step - loss: 4.3093e-04 - accuracy: 1.0000 - val_loss: 0.4525 - val_accuracy: 0.9529
Epoch 129/200
938/938 [==============================] - 3s 3ms/step - loss: 4.0651e-04 - accuracy: 1.0000 - val_loss: 0.4577 - val_accuracy: 0.9523
Epoch 130/200
938/938 [==============================] - 3s 3ms/step - loss: 3.9236e-04 - accuracy: 1.0000 - val_loss: 0.4604 - val_accuracy: 0.9525
Epoch 131/200
938/938 [==============================] - 3s 3ms/step - loss: 3.7346e-04 - accuracy: 1.0000 - val_loss: 0.4635 - val_accuracy: 0.9522
Epoch 132/200
938/938 [==============================] - 3s 3ms/step - loss: 3.6180e-04 - accuracy: 1.0000 - val_loss: 0.4630 - val_accuracy: 0.9529
Epoch 133/200
938/938 [==============================] - 3s 3ms/step - loss: 3.5008e-04 - accuracy: 1.0000 - val_loss: 0.4634 - val_accuracy: 0.9521
Epoch 134/200
938/938 [==============================] - 3s 3ms/step - loss: 3.3836e-04 - accuracy: 1.0000 - val_loss: 0.4753 - val_accuracy: 0.9519
Epoch 135/200
938/938 [==============================] - 3s 3ms/step - loss: 3.3323e-04 - accuracy: 1.0000 - val_loss: 0.4684 - val_accuracy: 0.9521
Epoch 136/200
938/938 [==============================] - 3s 3ms/step - loss: 3.2477e-04 - accuracy: 1.0000 - val_loss: 0.4679 - val_accuracy: 0.9523
Epoch 137/200
938/938 [==============================] - 3s 3ms/step - loss: 3.1256e-04 - accuracy: 1.0000 - val_loss: 0.4728 - val_accuracy: 0.9518
Epoch 138/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9770e-04 - accuracy: 1.0000 - val_loss: 0.4733 - val_accuracy: 0.9524
Epoch 139/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9488e-04 - accuracy: 1.0000 - val_loss: 0.4765 - val_accuracy: 0.9520
Epoch 140/200
938/938 [==============================] - 3s 3ms/step - loss: 2.8132e-04 - accuracy: 1.0000 - val_loss: 0.4774 - val_accuracy: 0.9521
Epoch 141/200
938/938 [==============================] - 3s 3ms/step - loss: 2.7649e-04 - accuracy: 1.0000 - val_loss: 0.4782 - val_accuracy: 0.9521
Epoch 142/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6361e-04 - accuracy: 1.0000 - val_loss: 0.4845 - val_accuracy: 0.9514
Epoch 143/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6136e-04 - accuracy: 1.0000 - val_loss: 0.4796 - val_accuracy: 0.9518
Epoch 144/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4710e-04 - accuracy: 1.0000 - val_loss: 0.4831 - val_accuracy: 0.9518
Epoch 145/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4919e-04 - accuracy: 1.0000 - val_loss: 0.4831 - val_accuracy: 0.9516
Epoch 146/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3890e-04 - accuracy: 1.0000 - val_loss: 0.4849 - val_accuracy: 0.9520
Epoch 147/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2987e-04 - accuracy: 1.0000 - val_loss: 0.4851 - val_accuracy: 0.9517
Epoch 148/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2130e-04 - accuracy: 1.0000 - val_loss: 0.4855 - val_accuracy: 0.9515
Epoch 149/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1278e-04 - accuracy: 1.0000 - val_loss: 0.4867 - val_accuracy: 0.9514
Epoch 150/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8723e-04 - accuracy: 1.0000 - val_loss: 0.4905 - val_accuracy: 0.9510
Epoch 151/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8264e-04 - accuracy: 1.0000 - val_loss: 0.4981 - val_accuracy: 0.9510
Epoch 152/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7668e-04 - accuracy: 1.0000 - val_loss: 0.4923 - val_accuracy: 0.9510
Epoch 153/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6909e-04 - accuracy: 1.0000 - val_loss: 0.4951 - val_accuracy: 0.9514
Epoch 154/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6577e-04 - accuracy: 1.0000 - val_loss: 0.4965 - val_accuracy: 0.9510
Epoch 155/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6164e-04 - accuracy: 1.0000 - val_loss: 0.4991 - val_accuracy: 0.9512
Epoch 156/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5775e-04 - accuracy: 1.0000 - val_loss: 0.4985 - val_accuracy: 0.9513
Epoch 157/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5414e-04 - accuracy: 1.0000 - val_loss: 0.4993 - val_accuracy: 0.9509
Epoch 158/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5093e-04 - accuracy: 1.0000 - val_loss: 0.5011 - val_accuracy: 0.9512
Epoch 159/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4773e-04 - accuracy: 1.0000 - val_loss: 0.5032 - val_accuracy: 0.9512
Epoch 160/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4504e-04 - accuracy: 1.0000 - val_loss: 0.5026 - val_accuracy: 0.9512
Epoch 161/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4218e-04 - accuracy: 1.0000 - val_loss: 0.5032 - val_accuracy: 0.9512
Epoch 162/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3955e-04 - accuracy: 1.0000 - val_loss: 0.5051 - val_accuracy: 0.9513
Epoch 163/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3701e-04 - accuracy: 1.0000 - val_loss: 0.5064 - val_accuracy: 0.9509
Epoch 164/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3440e-04 - accuracy: 1.0000 - val_loss: 0.5055 - val_accuracy: 0.9511
Epoch 165/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3227e-04 - accuracy: 1.0000 - val_loss: 0.5074 - val_accuracy: 0.9511
Epoch 166/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3002e-04 - accuracy: 1.0000 - val_loss: 0.5083 - val_accuracy: 0.9511
Epoch 167/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2773e-04 - accuracy: 1.0000 - val_loss: 0.5096 - val_accuracy: 0.9511
Epoch 168/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2580e-04 - accuracy: 1.0000 - val_loss: 0.5106 - val_accuracy: 0.9510
Epoch 169/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2361e-04 - accuracy: 1.0000 - val_loss: 0.5109 - val_accuracy: 0.9509
Epoch 170/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2178e-04 - accuracy: 1.0000 - val_loss: 0.5129 - val_accuracy: 0.9510
Epoch 171/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1992e-04 - accuracy: 1.0000 - val_loss: 0.5140 - val_accuracy: 0.9512
Epoch 172/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1804e-04 - accuracy: 1.0000 - val_loss: 0.5131 - val_accuracy: 0.9510
Epoch 173/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1638e-04 - accuracy: 1.0000 - val_loss: 0.5149 - val_accuracy: 0.9508
Epoch 174/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1473e-04 - accuracy: 1.0000 - val_loss: 0.5154 - val_accuracy: 0.9510
Epoch 175/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1306e-04 - accuracy: 1.0000 - val_loss: 0.5170 - val_accuracy: 0.9507
Epoch 176/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1139e-04 - accuracy: 1.0000 - val_loss: 0.5175 - val_accuracy: 0.9511
Epoch 177/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0995e-04 - accuracy: 1.0000 - val_loss: 0.5182 - val_accuracy: 0.9509
Epoch 178/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0828e-04 - accuracy: 1.0000 - val_loss: 0.5192 - val_accuracy: 0.9513
Epoch 179/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0692e-04 - accuracy: 1.0000 - val_loss: 0.5191 - val_accuracy: 0.9510
Epoch 180/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0545e-04 - accuracy: 1.0000 - val_loss: 0.5209 - val_accuracy: 0.9509
Epoch 181/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0408e-04 - accuracy: 1.0000 - val_loss: 0.5210 - val_accuracy: 0.9510
Epoch 182/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0274e-04 - accuracy: 1.0000 - val_loss: 0.5227 - val_accuracy: 0.9510
Epoch 183/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0133e-04 - accuracy: 1.0000 - val_loss: 0.5239 - val_accuracy: 0.9512
Epoch 184/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0012e-04 - accuracy: 1.0000 - val_loss: 0.5240 - val_accuracy: 0.9506
Epoch 185/200
938/938 [==============================] - 3s 3ms/step - loss: 9.8911e-05 - accuracy: 1.0000 - val_loss: 0.5238 - val_accuracy: 0.9507
Epoch 186/200
938/938 [==============================] - 3s 3ms/step - loss: 9.7578e-05 - accuracy: 1.0000 - val_loss: 0.5242 - val_accuracy: 0.9510
Epoch 187/200
938/938 [==============================] - 3s 3ms/step - loss: 9.6527e-05 - accuracy: 1.0000 - val_loss: 0.5257 - val_accuracy: 0.9507
Epoch 188/200
938/938 [==============================] - 3s 3ms/step - loss: 9.5328e-05 - accuracy: 1.0000 - val_loss: 0.5261 - val_accuracy: 0.9508
Epoch 189/200
938/938 [==============================] - 3s 3ms/step - loss: 9.4206e-05 - accuracy: 1.0000 - val_loss: 0.5275 - val_accuracy: 0.9510
Epoch 190/200
938/938 [==============================] - 3s 3ms/step - loss: 9.3074e-05 - accuracy: 1.0000 - val_loss: 0.5273 - val_accuracy: 0.9511
Epoch 191/200
938/938 [==============================] - 3s 3ms/step - loss: 9.1907e-05 - accuracy: 1.0000 - val_loss: 0.5273 - val_accuracy: 0.9513
Epoch 192/200
938/938 [==============================] - 3s 3ms/step - loss: 9.0945e-05 - accuracy: 1.0000 - val_loss: 0.5291 - val_accuracy: 0.9510
Epoch 193/200
938/938 [==============================] - 3s 3ms/step - loss: 8.9916e-05 - accuracy: 1.0000 - val_loss: 0.5298 - val_accuracy: 0.9510
Epoch 194/200
938/938 [==============================] - 3s 3ms/step - loss: 8.8934e-05 - accuracy: 1.0000 - val_loss: 0.5303 - val_accuracy: 0.9507
Epoch 195/200
938/938 [==============================] - 3s 3ms/step - loss: 8.7953e-05 - accuracy: 1.0000 - val_loss: 0.5310 - val_accuracy: 0.9512
Epoch 196/200
938/938 [==============================] - 3s 3ms/step - loss: 8.7007e-05 - accuracy: 1.0000 - val_loss: 0.5313 - val_accuracy: 0.9514
Epoch 197/200
938/938 [==============================] - 3s 3ms/step - loss: 8.6052e-05 - accuracy: 1.0000 - val_loss: 0.5315 - val_accuracy: 0.9514
Epoch 198/200
938/938 [==============================] - 3s 3ms/step - loss: 8.5118e-05 - accuracy: 1.0000 - val_loss: 0.5327 - val_accuracy: 0.9511
Epoch 199/200
938/938 [==============================] - 3s 3ms/step - loss: 8.4178e-05 - accuracy: 1.0000 - val_loss: 0.5335 - val_accuracy: 0.9512
Epoch 200/200
938/938 [==============================] - 3s 3ms/step - loss: 8.3281e-05 - accuracy: 1.0000 - val_loss: 0.5352 - val_accuracy: 0.9510

(d) Activation function: ReLU; initialization: Xavier initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_11"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_11 (Flatten)         (None, 784)               0         
_________________________________________________________________
dense_66 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_67 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_68 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_69 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_70 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_71 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.SGD(learning_rate=.01) #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
history3 = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
938/938 [==============================] - 3s 3ms/step - loss: 0.8977 - accuracy: 0.7730 - val_loss: 0.3496 - val_accuracy: 0.9001
Epoch 2/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2921 - accuracy: 0.9151 - val_loss: 0.2622 - val_accuracy: 0.9233
Epoch 3/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2230 - accuracy: 0.9346 - val_loss: 0.1998 - val_accuracy: 0.9398
Epoch 4/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1809 - accuracy: 0.9469 - val_loss: 0.1613 - val_accuracy: 0.9509
Epoch 5/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1529 - accuracy: 0.9557 - val_loss: 0.1406 - val_accuracy: 0.9578
Epoch 6/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1315 - accuracy: 0.9618 - val_loss: 0.1319 - val_accuracy: 0.9613
Epoch 7/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1152 - accuracy: 0.9667 - val_loss: 0.1193 - val_accuracy: 0.9653
Epoch 8/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1013 - accuracy: 0.9704 - val_loss: 0.1093 - val_accuracy: 0.9662
Epoch 9/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0897 - accuracy: 0.9738 - val_loss: 0.1294 - val_accuracy: 0.9598
Epoch 10/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0801 - accuracy: 0.9765 - val_loss: 0.1067 - val_accuracy: 0.9678
Epoch 11/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0714 - accuracy: 0.9793 - val_loss: 0.0953 - val_accuracy: 0.9714
Epoch 12/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0635 - accuracy: 0.9815 - val_loss: 0.1125 - val_accuracy: 0.9672
Epoch 13/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0564 - accuracy: 0.9839 - val_loss: 0.0842 - val_accuracy: 0.9742
Epoch 14/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0515 - accuracy: 0.9852 - val_loss: 0.0819 - val_accuracy: 0.9752
Epoch 15/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0462 - accuracy: 0.9868 - val_loss: 0.0803 - val_accuracy: 0.9762
Epoch 16/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0417 - accuracy: 0.9880 - val_loss: 0.0813 - val_accuracy: 0.9756
Epoch 17/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0367 - accuracy: 0.9899 - val_loss: 0.0807 - val_accuracy: 0.9759
Epoch 18/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0330 - accuracy: 0.9912 - val_loss: 0.0797 - val_accuracy: 0.9762
Epoch 19/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0298 - accuracy: 0.9920 - val_loss: 0.0747 - val_accuracy: 0.9774
Epoch 20/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0264 - accuracy: 0.9931 - val_loss: 0.0776 - val_accuracy: 0.9773
Epoch 21/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0234 - accuracy: 0.9942 - val_loss: 0.0774 - val_accuracy: 0.9778
Epoch 22/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0207 - accuracy: 0.9952 - val_loss: 0.0761 - val_accuracy: 0.9779
Epoch 23/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0185 - accuracy: 0.9957 - val_loss: 0.0824 - val_accuracy: 0.9753
Epoch 24/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0165 - accuracy: 0.9964 - val_loss: 0.0769 - val_accuracy: 0.9779
Epoch 25/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0146 - accuracy: 0.9971 - val_loss: 0.0788 - val_accuracy: 0.9778
Epoch 26/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0128 - accuracy: 0.9974 - val_loss: 0.0762 - val_accuracy: 0.9792
Epoch 27/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0112 - accuracy: 0.9981 - val_loss: 0.0785 - val_accuracy: 0.9785
Epoch 28/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0101 - accuracy: 0.9983 - val_loss: 0.0858 - val_accuracy: 0.9772
Epoch 29/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0087 - accuracy: 0.9988 - val_loss: 0.0772 - val_accuracy: 0.9793
Epoch 30/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0075 - accuracy: 0.9991 - val_loss: 0.0820 - val_accuracy: 0.9774
Epoch 31/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0070 - accuracy: 0.9990 - val_loss: 0.0823 - val_accuracy: 0.9780
Epoch 32/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0061 - accuracy: 0.9994 - val_loss: 0.0783 - val_accuracy: 0.9793
Epoch 33/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0053 - accuracy: 0.9995 - val_loss: 0.0795 - val_accuracy: 0.9796
Epoch 34/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0048 - accuracy: 0.9996 - val_loss: 0.0842 - val_accuracy: 0.9788
Epoch 35/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0043 - accuracy: 0.9996 - val_loss: 0.0801 - val_accuracy: 0.9795
Epoch 36/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0037 - accuracy: 0.9998 - val_loss: 0.0807 - val_accuracy: 0.9794
Epoch 37/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0034 - accuracy: 0.9998 - val_loss: 0.0833 - val_accuracy: 0.9792
Epoch 38/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0030 - accuracy: 0.9998 - val_loss: 0.0837 - val_accuracy: 0.9791
Epoch 39/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0027 - accuracy: 0.9999 - val_loss: 0.0840 - val_accuracy: 0.9793
Epoch 40/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0025 - accuracy: 0.9999 - val_loss: 0.0850 - val_accuracy: 0.9786
Epoch 41/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0023 - accuracy: 0.9999 - val_loss: 0.0845 - val_accuracy: 0.9799
Epoch 42/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.0865 - val_accuracy: 0.9787
Epoch 43/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9795
Epoch 44/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0018 - accuracy: 0.9999 - val_loss: 0.0882 - val_accuracy: 0.9793
Epoch 45/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0881 - val_accuracy: 0.9791
Epoch 46/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 0.9798
Epoch 47/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9793
Epoch 48/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0900 - val_accuracy: 0.9795
Epoch 49/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0903 - val_accuracy: 0.9795
Epoch 50/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0905 - val_accuracy: 0.9795
Epoch 51/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0910 - val_accuracy: 0.9798
Epoch 52/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0919 - val_accuracy: 0.9795
Epoch 53/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9797
Epoch 54/200
938/938 [==============================] - 3s 3ms/step - loss: 9.6542e-04 - accuracy: 1.0000 - val_loss: 0.0926 - val_accuracy: 0.9794
Epoch 55/200
938/938 [==============================] - 3s 3ms/step - loss: 9.1859e-04 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9796
Epoch 56/200
938/938 [==============================] - 3s 3ms/step - loss: 8.9172e-04 - accuracy: 1.0000 - val_loss: 0.0940 - val_accuracy: 0.9792
Epoch 57/200
938/938 [==============================] - 3s 3ms/step - loss: 8.4012e-04 - accuracy: 1.0000 - val_loss: 0.0937 - val_accuracy: 0.9792
Epoch 58/200
938/938 [==============================] - 3s 3ms/step - loss: 8.0657e-04 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9793
Epoch 59/200
938/938 [==============================] - 3s 3ms/step - loss: 7.7955e-04 - accuracy: 1.0000 - val_loss: 0.0947 - val_accuracy: 0.9796
Epoch 60/200
938/938 [==============================] - 3s 3ms/step - loss: 7.4536e-04 - accuracy: 1.0000 - val_loss: 0.0949 - val_accuracy: 0.9794
Epoch 61/200
938/938 [==============================] - 3s 3ms/step - loss: 7.1695e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9795
Epoch 62/200
938/938 [==============================] - 3s 3ms/step - loss: 6.9276e-04 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 0.9793
Epoch 63/200
938/938 [==============================] - 3s 3ms/step - loss: 6.6305e-04 - accuracy: 1.0000 - val_loss: 0.0965 - val_accuracy: 0.9792
Epoch 64/200
938/938 [==============================] - 3s 3ms/step - loss: 6.4406e-04 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 0.9796
Epoch 65/200
938/938 [==============================] - 3s 3ms/step - loss: 6.1780e-04 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9793
Epoch 66/200
938/938 [==============================] - 3s 3ms/step - loss: 6.0194e-04 - accuracy: 1.0000 - val_loss: 0.0971 - val_accuracy: 0.9793
Epoch 67/200
938/938 [==============================] - 3s 3ms/step - loss: 5.8031e-04 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9795
Epoch 68/200
938/938 [==============================] - 3s 3ms/step - loss: 5.6361e-04 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9794
Epoch 69/200
938/938 [==============================] - 3s 3ms/step - loss: 5.4437e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9795
Epoch 70/200
938/938 [==============================] - 3s 3ms/step - loss: 5.2660e-04 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9796
Epoch 71/200
938/938 [==============================] - 3s 3ms/step - loss: 5.1449e-04 - accuracy: 1.0000 - val_loss: 0.0988 - val_accuracy: 0.9792
Epoch 72/200
938/938 [==============================] - 3s 3ms/step - loss: 4.9714e-04 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9797
Epoch 73/200
938/938 [==============================] - 3s 3ms/step - loss: 4.8457e-04 - accuracy: 1.0000 - val_loss: 0.0989 - val_accuracy: 0.9792
Epoch 74/200
938/938 [==============================] - 3s 3ms/step - loss: 4.7051e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9796
Epoch 75/200
938/938 [==============================] - 3s 3ms/step - loss: 4.6025e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9798
Epoch 76/200
938/938 [==============================] - 3s 3ms/step - loss: 4.4789e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9796
Epoch 77/200
938/938 [==============================] - 3s 3ms/step - loss: 4.3601e-04 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9796
Epoch 78/200
938/938 [==============================] - 3s 3ms/step - loss: 4.2445e-04 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 0.9793
Epoch 79/200
938/938 [==============================] - 3s 3ms/step - loss: 4.1331e-04 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9797
Epoch 80/200
938/938 [==============================] - 3s 3ms/step - loss: 4.0416e-04 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9799
Epoch 81/200
938/938 [==============================] - 3s 3ms/step - loss: 3.9688e-04 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9791
Epoch 82/200
938/938 [==============================] - 3s 3ms/step - loss: 3.8537e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9794
Epoch 83/200
938/938 [==============================] - 3s 3ms/step - loss: 3.7603e-04 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9797
Epoch 84/200
938/938 [==============================] - 3s 3ms/step - loss: 3.6579e-04 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9796
Epoch 85/200
938/938 [==============================] - 3s 3ms/step - loss: 3.5945e-04 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9797
Epoch 86/200
938/938 [==============================] - 3s 3ms/step - loss: 3.5085e-04 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9793
Epoch 87/200
938/938 [==============================] - 3s 3ms/step - loss: 3.4380e-04 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9793
Epoch 88/200
938/938 [==============================] - 3s 3ms/step - loss: 3.3564e-04 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9791
Epoch 89/200
938/938 [==============================] - 3s 3ms/step - loss: 3.2963e-04 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9793
Epoch 90/200
938/938 [==============================] - 3s 3ms/step - loss: 3.2194e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9795
Epoch 91/200
938/938 [==============================] - 3s 3ms/step - loss: 3.1592e-04 - accuracy: 1.0000 - val_loss: 0.1043 - val_accuracy: 0.9794
Epoch 92/200
938/938 [==============================] - 3s 3ms/step - loss: 3.0831e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9796
Epoch 93/200
938/938 [==============================] - 3s 3ms/step - loss: 3.0449e-04 - accuracy: 1.0000 - val_loss: 0.1048 - val_accuracy: 0.9795
Epoch 94/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9821e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9796
Epoch 95/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9174e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9793
Epoch 96/200
938/938 [==============================] - 3s 3ms/step - loss: 2.8701e-04 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9795
Epoch 97/200
938/938 [==============================] - 3s 3ms/step - loss: 2.8113e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9794
Epoch 98/200
938/938 [==============================] - 3s 3ms/step - loss: 2.7680e-04 - accuracy: 1.0000 - val_loss: 0.1054 - val_accuracy: 0.9795
Epoch 99/200
938/938 [==============================] - 3s 3ms/step - loss: 2.7150e-04 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 0.9795
Epoch 100/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6644e-04 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9795
Epoch 101/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6226e-04 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9795
Epoch 102/200
938/938 [==============================] - 3s 3ms/step - loss: 2.5747e-04 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9795
Epoch 103/200
938/938 [==============================] - 3s 3ms/step - loss: 2.5215e-04 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9792
Epoch 104/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4906e-04 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9793
Epoch 105/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4488e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9794
Epoch 106/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4007e-04 - accuracy: 1.0000 - val_loss: 0.1073 - val_accuracy: 0.9793
Epoch 107/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3686e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9794
Epoch 108/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3300e-04 - accuracy: 1.0000 - val_loss: 0.1077 - val_accuracy: 0.9795
Epoch 109/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2931e-04 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 0.9795
Epoch 110/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2575e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9793
Epoch 111/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2165e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9795
Epoch 112/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1828e-04 - accuracy: 1.0000 - val_loss: 0.1080 - val_accuracy: 0.9795
Epoch 113/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1533e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9797
Epoch 114/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1235e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9795
Epoch 115/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0896e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9794
Epoch 116/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0651e-04 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9795
Epoch 117/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0269e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9794
Epoch 118/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0018e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9794
Epoch 119/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9724e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9794
Epoch 120/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9458e-04 - accuracy: 1.0000 - val_loss: 0.1094 - val_accuracy: 0.9793
Epoch 121/200
938/938 [==============================] - 4s 4ms/step - loss: 1.9208e-04 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9796
Epoch 122/200
938/938 [==============================] - 3s 4ms/step - loss: 1.8917e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9794
Epoch 123/200
938/938 [==============================] - 3s 4ms/step - loss: 1.8650e-04 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9794
Epoch 124/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8429e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9793
Epoch 125/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8174e-04 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9796
Epoch 126/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7905e-04 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9795
Epoch 127/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7691e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9797
Epoch 128/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7483e-04 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9793
Epoch 129/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7300e-04 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9795
Epoch 130/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7062e-04 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9793
Epoch 131/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6841e-04 - accuracy: 1.0000 - val_loss: 0.1112 - val_accuracy: 0.9795
Epoch 132/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6598e-04 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9795
Epoch 133/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6433e-04 - accuracy: 1.0000 - val_loss: 0.1115 - val_accuracy: 0.9795
Epoch 134/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6219e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9795
Epoch 135/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6012e-04 - accuracy: 1.0000 - val_loss: 0.1116 - val_accuracy: 0.9796
Epoch 136/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5838e-04 - accuracy: 1.0000 - val_loss: 0.1117 - val_accuracy: 0.9796
Epoch 137/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5624e-04 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9795
Epoch 138/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5393e-04 - accuracy: 1.0000 - val_loss: 0.1121 - val_accuracy: 0.9796
Epoch 139/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5289e-04 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9795
Epoch 140/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5069e-04 - accuracy: 1.0000 - val_loss: 0.1124 - val_accuracy: 0.9792
Epoch 141/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4943e-04 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9795
Epoch 142/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4753e-04 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9794
Epoch 143/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4609e-04 - accuracy: 1.0000 - val_loss: 0.1127 - val_accuracy: 0.9795
Epoch 144/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4431e-04 - accuracy: 1.0000 - val_loss: 0.1130 - val_accuracy: 0.9792
Epoch 145/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4291e-04 - accuracy: 1.0000 - val_loss: 0.1131 - val_accuracy: 0.9793
Epoch 146/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4122e-04 - accuracy: 1.0000 - val_loss: 0.1132 - val_accuracy: 0.9795
Epoch 147/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3981e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9794
Epoch 148/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3830e-04 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9795
Epoch 149/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3680e-04 - accuracy: 1.0000 - val_loss: 0.1136 - val_accuracy: 0.9794
Epoch 150/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3523e-04 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9796
Epoch 151/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3412e-04 - accuracy: 1.0000 - val_loss: 0.1139 - val_accuracy: 0.9795
Epoch 152/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3264e-04 - accuracy: 1.0000 - val_loss: 0.1138 - val_accuracy: 0.9795
Epoch 153/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3086e-04 - accuracy: 1.0000 - val_loss: 0.1140 - val_accuracy: 0.9792
Epoch 154/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2994e-04 - accuracy: 1.0000 - val_loss: 0.1142 - val_accuracy: 0.9793
Epoch 155/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2864e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9793
Epoch 156/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2743e-04 - accuracy: 1.0000 - val_loss: 0.1144 - val_accuracy: 0.9793
Epoch 157/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2604e-04 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9796
Epoch 158/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2463e-04 - accuracy: 1.0000 - val_loss: 0.1146 - val_accuracy: 0.9796
Epoch 159/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2339e-04 - accuracy: 1.0000 - val_loss: 0.1148 - val_accuracy: 0.9794
Epoch 160/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2237e-04 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9796
Epoch 161/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2100e-04 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9795
Epoch 162/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1989e-04 - accuracy: 1.0000 - val_loss: 0.1151 - val_accuracy: 0.9794
Epoch 163/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1858e-04 - accuracy: 1.0000 - val_loss: 0.1150 - val_accuracy: 0.9793
Epoch 164/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1787e-04 - accuracy: 1.0000 - val_loss: 0.1154 - val_accuracy: 0.9795
Epoch 165/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1644e-04 - accuracy: 1.0000 - val_loss: 0.1155 - val_accuracy: 0.9795
Epoch 166/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1555e-04 - accuracy: 1.0000 - val_loss: 0.1156 - val_accuracy: 0.9795
Epoch 167/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1433e-04 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9797
Epoch 168/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1352e-04 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9793
Epoch 169/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1241e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9797
Epoch 170/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1142e-04 - accuracy: 1.0000 - val_loss: 0.1159 - val_accuracy: 0.9792
Epoch 171/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1043e-04 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9794
Epoch 172/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0937e-04 - accuracy: 1.0000 - val_loss: 0.1161 - val_accuracy: 0.9795
Epoch 173/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0840e-04 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9794
Epoch 174/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0733e-04 - accuracy: 1.0000 - val_loss: 0.1163 - val_accuracy: 0.9795
Epoch 175/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0651e-04 - accuracy: 1.0000 - val_loss: 0.1165 - val_accuracy: 0.9795
Epoch 176/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0554e-04 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9795
Epoch 177/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0467e-04 - accuracy: 1.0000 - val_loss: 0.1167 - val_accuracy: 0.9794
Epoch 178/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0368e-04 - accuracy: 1.0000 - val_loss: 0.1166 - val_accuracy: 0.9792
Epoch 179/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0295e-04 - accuracy: 1.0000 - val_loss: 0.1169 - val_accuracy: 0.9794
Epoch 180/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0207e-04 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9794
Epoch 181/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0123e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9795
Epoch 182/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0023e-04 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9795
Epoch 183/200
938/938 [==============================] - 3s 3ms/step - loss: 9.9611e-05 - accuracy: 1.0000 - val_loss: 0.1172 - val_accuracy: 0.9793
Epoch 184/200
938/938 [==============================] - 3s 3ms/step - loss: 9.8657e-05 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9795
Epoch 185/200
938/938 [==============================] - 3s 3ms/step - loss: 9.8070e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9793
Epoch 186/200
938/938 [==============================] - 3s 3ms/step - loss: 9.7171e-05 - accuracy: 1.0000 - val_loss: 0.1174 - val_accuracy: 0.9793
Epoch 187/200
938/938 [==============================] - 3s 3ms/step - loss: 9.6257e-05 - accuracy: 1.0000 - val_loss: 0.1178 - val_accuracy: 0.9795
Epoch 188/200
938/938 [==============================] - 3s 3ms/step - loss: 9.5757e-05 - accuracy: 1.0000 - val_loss: 0.1176 - val_accuracy: 0.9792
Epoch 189/200
938/938 [==============================] - 3s 3ms/step - loss: 9.4810e-05 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9793
Epoch 190/200
938/938 [==============================] - 3s 3ms/step - loss: 9.4068e-05 - accuracy: 1.0000 - val_loss: 0.1179 - val_accuracy: 0.9794
Epoch 191/200
938/938 [==============================] - 3s 3ms/step - loss: 9.3322e-05 - accuracy: 1.0000 - val_loss: 0.1180 - val_accuracy: 0.9793
Epoch 192/200
938/938 [==============================] - 3s 3ms/step - loss: 9.2460e-05 - accuracy: 1.0000 - val_loss: 0.1181 - val_accuracy: 0.9793
Epoch 193/200
938/938 [==============================] - 3s 3ms/step - loss: 9.2000e-05 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9794
Epoch 194/200
938/938 [==============================] - 3s 3ms/step - loss: 9.1224e-05 - accuracy: 1.0000 - val_loss: 0.1182 - val_accuracy: 0.9794
Epoch 195/200
938/938 [==============================] - 3s 3ms/step - loss: 9.0363e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9794
Epoch 196/200
938/938 [==============================] - 3s 3ms/step - loss: 8.9874e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9794
Epoch 197/200
938/938 [==============================] - 3s 3ms/step - loss: 8.9103e-05 - accuracy: 1.0000 - val_loss: 0.1185 - val_accuracy: 0.9794
Epoch 198/200
938/938 [==============================] - 3s 3ms/step - loss: 8.8384e-05 - accuracy: 1.0000 - val_loss: 0.1188 - val_accuracy: 0.9795
Epoch 199/200
938/938 [==============================] - 3s 3ms/step - loss: 8.7737e-05 - accuracy: 1.0000 - val_loss: 0.1190 - val_accuracy: 0.9796
Epoch 200/200
938/938 [==============================] - 3s 3ms/step - loss: 8.7152e-05 - accuracy: 1.0000 - val_loss: 0.1187 - val_accuracy: 0.9792

(e) Activation function: ReLU; initialization: Kaiming He’s initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.HeNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_12"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_12 (Flatten)         (None, 784)               0         
_________________________________________________________________
dense_72 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_73 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_74 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_75 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_76 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_77 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.SGD(learning_rate=.01) #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
history4 = model.fit(X_train, y_train, batch_size=64, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
938/938 [==============================] - 3s 3ms/step - loss: 0.4928 - accuracy: 0.8651 - val_loss: 0.2472 - val_accuracy: 0.9233
Epoch 2/200
938/938 [==============================] - 3s 3ms/step - loss: 0.2065 - accuracy: 0.9394 - val_loss: 0.1962 - val_accuracy: 0.9422
Epoch 3/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1548 - accuracy: 0.9543 - val_loss: 0.1572 - val_accuracy: 0.9541
Epoch 4/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1245 - accuracy: 0.9632 - val_loss: 0.1322 - val_accuracy: 0.9588
Epoch 5/200
938/938 [==============================] - 3s 3ms/step - loss: 0.1033 - accuracy: 0.9694 - val_loss: 0.1237 - val_accuracy: 0.9627
Epoch 6/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0876 - accuracy: 0.9741 - val_loss: 0.1034 - val_accuracy: 0.9708
Epoch 7/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0748 - accuracy: 0.9779 - val_loss: 0.1010 - val_accuracy: 0.9693
Epoch 8/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0650 - accuracy: 0.9811 - val_loss: 0.0875 - val_accuracy: 0.9742
Epoch 9/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0569 - accuracy: 0.9834 - val_loss: 0.1152 - val_accuracy: 0.9633
Epoch 10/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0490 - accuracy: 0.9857 - val_loss: 0.0868 - val_accuracy: 0.9736
Epoch 11/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0427 - accuracy: 0.9878 - val_loss: 0.0795 - val_accuracy: 0.9761
Epoch 12/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0370 - accuracy: 0.9893 - val_loss: 0.0856 - val_accuracy: 0.9741
Epoch 13/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0318 - accuracy: 0.9917 - val_loss: 0.1016 - val_accuracy: 0.9693
Epoch 14/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0276 - accuracy: 0.9929 - val_loss: 0.0821 - val_accuracy: 0.9757
Epoch 15/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0232 - accuracy: 0.9944 - val_loss: 0.0863 - val_accuracy: 0.9739
Epoch 16/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0207 - accuracy: 0.9951 - val_loss: 0.0793 - val_accuracy: 0.9766
Epoch 17/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0179 - accuracy: 0.9961 - val_loss: 0.0839 - val_accuracy: 0.9746
Epoch 18/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0154 - accuracy: 0.9969 - val_loss: 0.0767 - val_accuracy: 0.9779
Epoch 19/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0130 - accuracy: 0.9978 - val_loss: 0.0772 - val_accuracy: 0.9775
Epoch 20/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0116 - accuracy: 0.9981 - val_loss: 0.0749 - val_accuracy: 0.9778
Epoch 21/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0097 - accuracy: 0.9985 - val_loss: 0.0777 - val_accuracy: 0.9769
Epoch 22/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0084 - accuracy: 0.9990 - val_loss: 0.0773 - val_accuracy: 0.9787
Epoch 23/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0073 - accuracy: 0.9992 - val_loss: 0.0784 - val_accuracy: 0.9793
Epoch 24/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0060 - accuracy: 0.9996 - val_loss: 0.0781 - val_accuracy: 0.9786
Epoch 25/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0053 - accuracy: 0.9995 - val_loss: 0.0767 - val_accuracy: 0.9785
Epoch 26/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0046 - accuracy: 0.9997 - val_loss: 0.0823 - val_accuracy: 0.9788
Epoch 27/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0041 - accuracy: 0.9998 - val_loss: 0.0784 - val_accuracy: 0.9796
Epoch 28/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0037 - accuracy: 0.9999 - val_loss: 0.0826 - val_accuracy: 0.9781
Epoch 29/200
938/938 [==============================] - 3s 4ms/step - loss: 0.0032 - accuracy: 0.9999 - val_loss: 0.0825 - val_accuracy: 0.9786
Epoch 30/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0029 - accuracy: 0.9999 - val_loss: 0.0806 - val_accuracy: 0.9793
Epoch 31/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0026 - accuracy: 0.9999 - val_loss: 0.0817 - val_accuracy: 0.9790
Epoch 32/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0821 - val_accuracy: 0.9793
Epoch 33/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0823 - val_accuracy: 0.9788
Epoch 34/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9788
Epoch 35/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0840 - val_accuracy: 0.9788
Epoch 36/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9794
Epoch 37/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0855 - val_accuracy: 0.9791
Epoch 38/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0856 - val_accuracy: 0.9791
Epoch 39/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 0.9791
Epoch 40/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0868 - val_accuracy: 0.9792
Epoch 41/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 0.9784
Epoch 42/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0876 - val_accuracy: 0.9784
Epoch 43/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9792
Epoch 44/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0878 - val_accuracy: 0.9797
Epoch 45/200
938/938 [==============================] - 3s 3ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0885 - val_accuracy: 0.9795
Epoch 46/200
938/938 [==============================] - 3s 3ms/step - loss: 9.9241e-04 - accuracy: 1.0000 - val_loss: 0.0884 - val_accuracy: 0.9794
Epoch 47/200
938/938 [==============================] - 3s 3ms/step - loss: 9.5525e-04 - accuracy: 1.0000 - val_loss: 0.0888 - val_accuracy: 0.9790
Epoch 48/200
938/938 [==============================] - 3s 3ms/step - loss: 9.1258e-04 - accuracy: 1.0000 - val_loss: 0.0898 - val_accuracy: 0.9789
Epoch 49/200
938/938 [==============================] - 3s 3ms/step - loss: 8.7487e-04 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 0.9792
Epoch 50/200
938/938 [==============================] - 3s 3ms/step - loss: 8.4040e-04 - accuracy: 1.0000 - val_loss: 0.0894 - val_accuracy: 0.9791
Epoch 51/200
938/938 [==============================] - 3s 3ms/step - loss: 8.1012e-04 - accuracy: 1.0000 - val_loss: 0.0904 - val_accuracy: 0.9791
Epoch 52/200
938/938 [==============================] - 3s 3ms/step - loss: 7.7838e-04 - accuracy: 1.0000 - val_loss: 0.0901 - val_accuracy: 0.9791
Epoch 53/200
938/938 [==============================] - 3s 3ms/step - loss: 7.5134e-04 - accuracy: 1.0000 - val_loss: 0.0915 - val_accuracy: 0.9795
Epoch 54/200
938/938 [==============================] - 3s 3ms/step - loss: 7.2590e-04 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 0.9792
Epoch 55/200
938/938 [==============================] - 3s 3ms/step - loss: 7.0316e-04 - accuracy: 1.0000 - val_loss: 0.0924 - val_accuracy: 0.9785
Epoch 56/200
938/938 [==============================] - 3s 3ms/step - loss: 6.8198e-04 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 0.9792
Epoch 57/200
938/938 [==============================] - 3s 3ms/step - loss: 6.5640e-04 - accuracy: 1.0000 - val_loss: 0.0920 - val_accuracy: 0.9793
Epoch 58/200
938/938 [==============================] - 3s 3ms/step - loss: 6.3500e-04 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9790
Epoch 59/200
938/938 [==============================] - 3s 3ms/step - loss: 6.1509e-04 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 0.9790
Epoch 60/200
938/938 [==============================] - 3s 3ms/step - loss: 5.9790e-04 - accuracy: 1.0000 - val_loss: 0.0932 - val_accuracy: 0.9793
Epoch 61/200
938/938 [==============================] - 3s 3ms/step - loss: 5.7882e-04 - accuracy: 1.0000 - val_loss: 0.0930 - val_accuracy: 0.9791
Epoch 62/200
938/938 [==============================] - 3s 3ms/step - loss: 5.6287e-04 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 0.9793
Epoch 63/200
938/938 [==============================] - 3s 3ms/step - loss: 5.4409e-04 - accuracy: 1.0000 - val_loss: 0.0939 - val_accuracy: 0.9793
Epoch 64/200
938/938 [==============================] - 3s 3ms/step - loss: 5.3308e-04 - accuracy: 1.0000 - val_loss: 0.0942 - val_accuracy: 0.9795
Epoch 65/200
938/938 [==============================] - 3s 3ms/step - loss: 5.1492e-04 - accuracy: 1.0000 - val_loss: 0.0944 - val_accuracy: 0.9798
Epoch 66/200
938/938 [==============================] - 3s 3ms/step - loss: 5.0429e-04 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9798
Epoch 67/200
938/938 [==============================] - 3s 3ms/step - loss: 4.9105e-04 - accuracy: 1.0000 - val_loss: 0.0945 - val_accuracy: 0.9794
Epoch 68/200
938/938 [==============================] - 3s 3ms/step - loss: 4.7805e-04 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9792
Epoch 69/200
938/938 [==============================] - 3s 3ms/step - loss: 4.6546e-04 - accuracy: 1.0000 - val_loss: 0.0952 - val_accuracy: 0.9791
Epoch 70/200
938/938 [==============================] - 3s 3ms/step - loss: 4.5494e-04 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 0.9795
Epoch 71/200
938/938 [==============================] - 3s 3ms/step - loss: 4.4527e-04 - accuracy: 1.0000 - val_loss: 0.0956 - val_accuracy: 0.9792
Epoch 72/200
938/938 [==============================] - 3s 3ms/step - loss: 4.3459e-04 - accuracy: 1.0000 - val_loss: 0.0954 - val_accuracy: 0.9794
Epoch 73/200
938/938 [==============================] - 3s 3ms/step - loss: 4.2344e-04 - accuracy: 1.0000 - val_loss: 0.0958 - val_accuracy: 0.9791
Epoch 74/200
938/938 [==============================] - 3s 3ms/step - loss: 4.1433e-04 - accuracy: 1.0000 - val_loss: 0.0961 - val_accuracy: 0.9793
Epoch 75/200
938/938 [==============================] - 3s 3ms/step - loss: 4.0497e-04 - accuracy: 1.0000 - val_loss: 0.0963 - val_accuracy: 0.9794
Epoch 76/200
938/938 [==============================] - 3s 3ms/step - loss: 3.9504e-04 - accuracy: 1.0000 - val_loss: 0.0967 - val_accuracy: 0.9795
Epoch 77/200
938/938 [==============================] - 3s 3ms/step - loss: 3.8727e-04 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 0.9797
Epoch 78/200
938/938 [==============================] - 3s 3ms/step - loss: 3.7946e-04 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9794
Epoch 79/200
938/938 [==============================] - 3s 3ms/step - loss: 3.7156e-04 - accuracy: 1.0000 - val_loss: 0.0973 - val_accuracy: 0.9792
Epoch 80/200
938/938 [==============================] - 3s 3ms/step - loss: 3.6358e-04 - accuracy: 1.0000 - val_loss: 0.0972 - val_accuracy: 0.9793
Epoch 81/200
938/938 [==============================] - 3s 3ms/step - loss: 3.5582e-04 - accuracy: 1.0000 - val_loss: 0.0975 - val_accuracy: 0.9789
Epoch 82/200
938/938 [==============================] - 3s 3ms/step - loss: 3.4802e-04 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9788
Epoch 83/200
938/938 [==============================] - 3s 3ms/step - loss: 3.4214e-04 - accuracy: 1.0000 - val_loss: 0.0982 - val_accuracy: 0.9793
Epoch 84/200
938/938 [==============================] - 3s 3ms/step - loss: 3.3511e-04 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9792
Epoch 85/200
938/938 [==============================] - 3s 3ms/step - loss: 3.2912e-04 - accuracy: 1.0000 - val_loss: 0.0985 - val_accuracy: 0.9798
Epoch 86/200
938/938 [==============================] - 3s 3ms/step - loss: 3.2387e-04 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 0.9792
Epoch 87/200
938/938 [==============================] - 3s 3ms/step - loss: 3.1683e-04 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 0.9790
Epoch 88/200
938/938 [==============================] - 3s 3ms/step - loss: 3.1116e-04 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 0.9797
Epoch 89/200
938/938 [==============================] - 3s 3ms/step - loss: 3.0520e-04 - accuracy: 1.0000 - val_loss: 0.0995 - val_accuracy: 0.9793
Epoch 90/200
938/938 [==============================] - 3s 3ms/step - loss: 3.0045e-04 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9793
Epoch 91/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9531e-04 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9796
Epoch 92/200
938/938 [==============================] - 3s 3ms/step - loss: 2.9000e-04 - accuracy: 1.0000 - val_loss: 0.0996 - val_accuracy: 0.9793
Epoch 93/200
938/938 [==============================] - 3s 3ms/step - loss: 2.8475e-04 - accuracy: 1.0000 - val_loss: 0.0999 - val_accuracy: 0.9795
Epoch 94/200
938/938 [==============================] - 3s 3ms/step - loss: 2.8072e-04 - accuracy: 1.0000 - val_loss: 0.0996 - val_accuracy: 0.9793
Epoch 95/200
938/938 [==============================] - 3s 3ms/step - loss: 2.7546e-04 - accuracy: 1.0000 - val_loss: 0.1002 - val_accuracy: 0.9792
Epoch 96/200
938/938 [==============================] - 3s 3ms/step - loss: 2.7144e-04 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 0.9795
Epoch 97/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6583e-04 - accuracy: 1.0000 - val_loss: 0.1003 - val_accuracy: 0.9791
Epoch 98/200
938/938 [==============================] - 3s 3ms/step - loss: 2.6156e-04 - accuracy: 1.0000 - val_loss: 0.1009 - val_accuracy: 0.9796
Epoch 99/200
938/938 [==============================] - 3s 3ms/step - loss: 2.5903e-04 - accuracy: 1.0000 - val_loss: 0.1008 - val_accuracy: 0.9795
Epoch 100/200
938/938 [==============================] - 3s 3ms/step - loss: 2.5408e-04 - accuracy: 1.0000 - val_loss: 0.1010 - val_accuracy: 0.9793
Epoch 101/200
938/938 [==============================] - 3s 3ms/step - loss: 2.5049e-04 - accuracy: 1.0000 - val_loss: 0.1013 - val_accuracy: 0.9795
Epoch 102/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4697e-04 - accuracy: 1.0000 - val_loss: 0.1012 - val_accuracy: 0.9796
Epoch 103/200
938/938 [==============================] - 3s 3ms/step - loss: 2.4326e-04 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9796
Epoch 104/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3966e-04 - accuracy: 1.0000 - val_loss: 0.1015 - val_accuracy: 0.9795
Epoch 105/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3599e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9793
Epoch 106/200
938/938 [==============================] - 3s 3ms/step - loss: 2.3225e-04 - accuracy: 1.0000 - val_loss: 0.1018 - val_accuracy: 0.9795
Epoch 107/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2901e-04 - accuracy: 1.0000 - val_loss: 0.1017 - val_accuracy: 0.9794
Epoch 108/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2594e-04 - accuracy: 1.0000 - val_loss: 0.1019 - val_accuracy: 0.9794
Epoch 109/200
938/938 [==============================] - 3s 3ms/step - loss: 2.2282e-04 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 0.9794
Epoch 110/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1955e-04 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9793
Epoch 111/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1644e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9795
Epoch 112/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1294e-04 - accuracy: 1.0000 - val_loss: 0.1025 - val_accuracy: 0.9794
Epoch 113/200
938/938 [==============================] - 3s 3ms/step - loss: 2.1068e-04 - accuracy: 1.0000 - val_loss: 0.1026 - val_accuracy: 0.9793
Epoch 114/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0776e-04 - accuracy: 1.0000 - val_loss: 0.1028 - val_accuracy: 0.9795
Epoch 115/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0502e-04 - accuracy: 1.0000 - val_loss: 0.1027 - val_accuracy: 0.9794
Epoch 116/200
938/938 [==============================] - 3s 3ms/step - loss: 2.0213e-04 - accuracy: 1.0000 - val_loss: 0.1031 - val_accuracy: 0.9795
Epoch 117/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9974e-04 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9793
Epoch 118/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9762e-04 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9794
Epoch 119/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9475e-04 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9794
Epoch 120/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9237e-04 - accuracy: 1.0000 - val_loss: 0.1035 - val_accuracy: 0.9796
Epoch 121/200
938/938 [==============================] - 3s 3ms/step - loss: 1.9000e-04 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 0.9796
Epoch 122/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8774e-04 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9795
Epoch 123/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8559e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9795
Epoch 124/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8307e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9794
Epoch 125/200
938/938 [==============================] - 3s 3ms/step - loss: 1.8084e-04 - accuracy: 1.0000 - val_loss: 0.1040 - val_accuracy: 0.9794
Epoch 126/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7878e-04 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 0.9795
Epoch 127/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7669e-04 - accuracy: 1.0000 - val_loss: 0.1046 - val_accuracy: 0.9795
Epoch 128/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7464e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9796
Epoch 129/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7250e-04 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 0.9796
Epoch 130/200
938/938 [==============================] - 3s 3ms/step - loss: 1.7091e-04 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9796
Epoch 131/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6856e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9797
Epoch 132/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6671e-04 - accuracy: 1.0000 - val_loss: 0.1049 - val_accuracy: 0.9794
Epoch 133/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6482e-04 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9796
Epoch 134/200
938/938 [==============================] - 3s 4ms/step - loss: 1.6301e-04 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9794
Epoch 135/200
938/938 [==============================] - 3s 3ms/step - loss: 1.6122e-04 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 0.9795
Epoch 136/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5952e-04 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9794
Epoch 137/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5799e-04 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9795
Epoch 138/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5617e-04 - accuracy: 1.0000 - val_loss: 0.1053 - val_accuracy: 0.9794
Epoch 139/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5437e-04 - accuracy: 1.0000 - val_loss: 0.1055 - val_accuracy: 0.9794
Epoch 140/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5275e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9796
Epoch 141/200
938/938 [==============================] - 3s 3ms/step - loss: 1.5144e-04 - accuracy: 1.0000 - val_loss: 0.1057 - val_accuracy: 0.9796
Epoch 142/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4973e-04 - accuracy: 1.0000 - val_loss: 0.1058 - val_accuracy: 0.9794
Epoch 143/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4831e-04 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9796
Epoch 144/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4638e-04 - accuracy: 1.0000 - val_loss: 0.1062 - val_accuracy: 0.9796
Epoch 145/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4542e-04 - accuracy: 1.0000 - val_loss: 0.1059 - val_accuracy: 0.9796
Epoch 146/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4384e-04 - accuracy: 1.0000 - val_loss: 0.1063 - val_accuracy: 0.9797
Epoch 147/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4243e-04 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9796
Epoch 148/200
938/938 [==============================] - 3s 3ms/step - loss: 1.4094e-04 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9795
Epoch 149/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3968e-04 - accuracy: 1.0000 - val_loss: 0.1066 - val_accuracy: 0.9794
Epoch 150/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3827e-04 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9797
Epoch 151/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3694e-04 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 0.9795
Epoch 152/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3567e-04 - accuracy: 1.0000 - val_loss: 0.1069 - val_accuracy: 0.9796
Epoch 153/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3401e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9796
Epoch 154/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3297e-04 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9797
Epoch 155/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3184e-04 - accuracy: 1.0000 - val_loss: 0.1071 - val_accuracy: 0.9798
Epoch 156/200
938/938 [==============================] - 3s 3ms/step - loss: 1.3063e-04 - accuracy: 1.0000 - val_loss: 0.1072 - val_accuracy: 0.9797
Epoch 157/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2945e-04 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9795
Epoch 158/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2814e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9798
Epoch 159/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2713e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9797
Epoch 160/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2603e-04 - accuracy: 1.0000 - val_loss: 0.1075 - val_accuracy: 0.9796
Epoch 161/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2470e-04 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9797
Epoch 162/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2373e-04 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9795
Epoch 163/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2273e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9796
Epoch 164/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2161e-04 - accuracy: 1.0000 - val_loss: 0.1081 - val_accuracy: 0.9796
Epoch 165/200
938/938 [==============================] - 3s 3ms/step - loss: 1.2041e-04 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9795
Epoch 166/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1927e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9796
Epoch 167/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1838e-04 - accuracy: 1.0000 - val_loss: 0.1082 - val_accuracy: 0.9794
Epoch 168/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1745e-04 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9797
Epoch 169/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1648e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9797
Epoch 170/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1547e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9797
Epoch 171/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1463e-04 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 0.9798
Epoch 172/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1365e-04 - accuracy: 1.0000 - val_loss: 0.1086 - val_accuracy: 0.9796
Epoch 173/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1267e-04 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9796
Epoch 174/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1162e-04 - accuracy: 1.0000 - val_loss: 0.1088 - val_accuracy: 0.9797
Epoch 175/200
938/938 [==============================] - 3s 3ms/step - loss: 1.1072e-04 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9796
Epoch 176/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0988e-04 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9796
Epoch 177/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0872e-04 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9797
Epoch 178/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0818e-04 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 0.9797
Epoch 179/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0736e-04 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9795
Epoch 180/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0653e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9795
Epoch 181/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0560e-04 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9795
Epoch 182/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0469e-04 - accuracy: 1.0000 - val_loss: 0.1096 - val_accuracy: 0.9796
Epoch 183/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0395e-04 - accuracy: 1.0000 - val_loss: 0.1095 - val_accuracy: 0.9796
Epoch 184/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0317e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9797
Epoch 185/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0233e-04 - accuracy: 1.0000 - val_loss: 0.1097 - val_accuracy: 0.9796
Epoch 186/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0171e-04 - accuracy: 1.0000 - val_loss: 0.1098 - val_accuracy: 0.9797
Epoch 187/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0081e-04 - accuracy: 1.0000 - val_loss: 0.1099 - val_accuracy: 0.9797
Epoch 188/200
938/938 [==============================] - 3s 3ms/step - loss: 1.0012e-04 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9796
Epoch 189/200
938/938 [==============================] - 3s 3ms/step - loss: 9.9270e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9794
Epoch 190/200
938/938 [==============================] - 3s 3ms/step - loss: 9.8603e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9799
Epoch 191/200
938/938 [==============================] - 3s 3ms/step - loss: 9.7870e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9796
Epoch 192/200
938/938 [==============================] - 3s 3ms/step - loss: 9.7116e-05 - accuracy: 1.0000 - val_loss: 0.1103 - val_accuracy: 0.9795
Epoch 193/200
938/938 [==============================] - 3s 3ms/step - loss: 9.6447e-05 - accuracy: 1.0000 - val_loss: 0.1105 - val_accuracy: 0.9797
Epoch 194/200
938/938 [==============================] - 3s 3ms/step - loss: 9.5707e-05 - accuracy: 1.0000 - val_loss: 0.1102 - val_accuracy: 0.9796
Epoch 195/200
938/938 [==============================] - 3s 3ms/step - loss: 9.4960e-05 - accuracy: 1.0000 - val_loss: 0.1104 - val_accuracy: 0.9797
Epoch 196/200
938/938 [==============================] - 3s 3ms/step - loss: 9.4395e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9797
Epoch 197/200
938/938 [==============================] - 3s 3ms/step - loss: 9.3783e-05 - accuracy: 1.0000 - val_loss: 0.1107 - val_accuracy: 0.9797
Epoch 198/200
938/938 [==============================] - 3s 3ms/step - loss: 9.3070e-05 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9797
Epoch 199/200
938/938 [==============================] - 3s 3ms/step - loss: 9.2503e-05 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9795
Epoch 200/200
938/938 [==============================] - 3s 3ms/step - loss: 9.1673e-05 - accuracy: 1.0000 - val_loss: 0.1110 - val_accuracy: 0.9798

Visualize SGD Results

In [ ]:
#loss_train = history.history['accuracy']
test_acc = history.history['val_accuracy'][0:50]
test_acc1 = history1.history['val_accuracy'][0:50]
test_acc2 = history2.history['val_accuracy'][0:50]
test_acc3 = history3.history['val_accuracy'][0:50]
test_acc4 = history4.history['val_accuracy'][0:50]

epochs = range(0,50)
plt.figure(figsize=(20,10))
plt.plot(epochs, test_acc, 'r', label='Logistic: Normal')
plt.plot(epochs, test_acc1, 'r', label='Logistic: Xavier', linewidth=3)
plt.plot(epochs, test_acc2, 'b', label='ReLU: Normal')
plt.plot(epochs, test_acc3, 'b', label='ReLU: Xavier', linewidth=3)
plt.plot(epochs, test_acc4, 'black', label='ReLU: He')
plt.title('SGD')
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy %')
plt.legend()
plt.show()
In [ ]:
#loss_train = history.history['accuracy']
test_acc = history.history['val_accuracy'][51:]
test_acc1 = history1.history['val_accuracy'][51:]
test_acc2 = history2.history['val_accuracy'][51:]
test_acc3 = history3.history['val_accuracy'][51:]
test_acc4 = history4.history['val_accuracy'][51:]

epochs = range(51,200)
plt.figure(figsize=(20,10))
plt.plot(epochs, test_acc, 'r', label='Logistic: Normal')
plt.plot(epochs, test_acc1, 'r', label='Logistic: Xavier', linewidth=3)
plt.plot(epochs, test_acc2, 'b', label='ReLU: Normal')
plt.plot(epochs, test_acc3, 'b', label='ReLU: Xavier', linewidth=3)
plt.plot(epochs, test_acc4, 'black', label='ReLU: He')
plt.title('SGD')
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy %')
plt.legend()
plt.show()

Adam

(a) Activation function: the logistic sigmoid function; initialization: random numbers gen-erated from the normal distribution (μ = 0, σ = 0.01)

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        #tf.keras.layers.Dense(512,kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_3 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_18 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_19 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_20 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_21 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_22 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_23 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam() #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
historya = model.fit(X_train, y_train, batch_size=128, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
469/469 [==============================] - 2s 4ms/step - loss: 2.1356 - accuracy: 0.1633 - val_loss: 1.9725 - val_accuracy: 0.2173
Epoch 2/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3498 - accuracy: 0.4763 - val_loss: 0.7668 - val_accuracy: 0.7515
Epoch 3/200
469/469 [==============================] - 2s 3ms/step - loss: 0.4624 - accuracy: 0.8750 - val_loss: 0.3131 - val_accuracy: 0.9143
Epoch 4/200
469/469 [==============================] - 2s 3ms/step - loss: 0.2746 - accuracy: 0.9271 - val_loss: 0.2702 - val_accuracy: 0.9296
Epoch 5/200
469/469 [==============================] - 2s 3ms/step - loss: 0.2111 - accuracy: 0.9431 - val_loss: 0.2163 - val_accuracy: 0.9406
Epoch 6/200
469/469 [==============================] - 2s 3ms/step - loss: 0.1697 - accuracy: 0.9540 - val_loss: 0.1626 - val_accuracy: 0.9569
Epoch 7/200
469/469 [==============================] - 2s 3ms/step - loss: 0.1388 - accuracy: 0.9618 - val_loss: 0.1526 - val_accuracy: 0.9587
Epoch 8/200
469/469 [==============================] - 2s 3ms/step - loss: 0.1154 - accuracy: 0.9675 - val_loss: 0.1289 - val_accuracy: 0.9647
Epoch 9/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0967 - accuracy: 0.9729 - val_loss: 0.1449 - val_accuracy: 0.9599
Epoch 10/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0841 - accuracy: 0.9757 - val_loss: 0.1163 - val_accuracy: 0.9674
Epoch 11/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0725 - accuracy: 0.9794 - val_loss: 0.1121 - val_accuracy: 0.9697
Epoch 12/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0592 - accuracy: 0.9831 - val_loss: 0.1169 - val_accuracy: 0.9676
Epoch 13/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0544 - accuracy: 0.9839 - val_loss: 0.1156 - val_accuracy: 0.9708
Epoch 14/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0474 - accuracy: 0.9862 - val_loss: 0.1006 - val_accuracy: 0.9728
Epoch 15/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0409 - accuracy: 0.9883 - val_loss: 0.1169 - val_accuracy: 0.9698
Epoch 16/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0350 - accuracy: 0.9896 - val_loss: 0.1027 - val_accuracy: 0.9737
Epoch 17/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0363 - accuracy: 0.9892 - val_loss: 0.1063 - val_accuracy: 0.9750
Epoch 18/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0284 - accuracy: 0.9914 - val_loss: 0.1031 - val_accuracy: 0.9766
Epoch 19/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0235 - accuracy: 0.9935 - val_loss: 0.1276 - val_accuracy: 0.9708
Epoch 20/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0233 - accuracy: 0.9933 - val_loss: 0.1445 - val_accuracy: 0.9687
Epoch 21/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.1194 - val_accuracy: 0.9750
Epoch 22/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0223 - accuracy: 0.9933 - val_loss: 0.1114 - val_accuracy: 0.9761
Epoch 23/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0168 - accuracy: 0.9951 - val_loss: 0.1060 - val_accuracy: 0.9753
Epoch 24/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.1274 - val_accuracy: 0.9753
Epoch 25/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0161 - accuracy: 0.9952 - val_loss: 0.1360 - val_accuracy: 0.9726
Epoch 26/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9961 - val_loss: 0.1209 - val_accuracy: 0.9762
Epoch 27/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0149 - accuracy: 0.9955 - val_loss: 0.1295 - val_accuracy: 0.9747
Epoch 28/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.1184 - val_accuracy: 0.9758
Epoch 29/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9967 - val_loss: 0.1222 - val_accuracy: 0.9756
Epoch 30/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9965 - val_loss: 0.1199 - val_accuracy: 0.9787
Epoch 31/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0102 - accuracy: 0.9970 - val_loss: 0.1112 - val_accuracy: 0.9800
Epoch 32/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 0.1135 - val_accuracy: 0.9789
Epoch 33/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0088 - accuracy: 0.9972 - val_loss: 0.1202 - val_accuracy: 0.9762
Epoch 34/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0110 - accuracy: 0.9968 - val_loss: 0.1163 - val_accuracy: 0.9783
Epoch 35/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0084 - accuracy: 0.9974 - val_loss: 0.1254 - val_accuracy: 0.9769
Epoch 36/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.1225 - val_accuracy: 0.9774
Epoch 37/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0078 - accuracy: 0.9975 - val_loss: 0.1295 - val_accuracy: 0.9778
Epoch 38/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.1286 - val_accuracy: 0.9783
Epoch 39/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0077 - accuracy: 0.9977 - val_loss: 0.1160 - val_accuracy: 0.9815
Epoch 40/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.1272 - val_accuracy: 0.9763
Epoch 41/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0071 - accuracy: 0.9979 - val_loss: 0.1195 - val_accuracy: 0.9802
Epoch 42/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.1175 - val_accuracy: 0.9793
Epoch 43/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0079 - accuracy: 0.9976 - val_loss: 0.1202 - val_accuracy: 0.9792
Epoch 44/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 0.1221 - val_accuracy: 0.9799
Epoch 45/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.1281 - val_accuracy: 0.9793
Epoch 46/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0091 - accuracy: 0.9973 - val_loss: 0.1166 - val_accuracy: 0.9798
Epoch 47/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.1245 - val_accuracy: 0.9793
Epoch 48/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.1297 - val_accuracy: 0.9782
Epoch 49/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 0.1304 - val_accuracy: 0.9781
Epoch 50/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1240 - val_accuracy: 0.9811
Epoch 51/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0090 - accuracy: 0.9974 - val_loss: 0.1090 - val_accuracy: 0.9821
Epoch 52/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1240 - val_accuracy: 0.9784
Epoch 53/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0047 - accuracy: 0.9986 - val_loss: 0.1153 - val_accuracy: 0.9819
Epoch 54/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.1208 - val_accuracy: 0.9803
Epoch 55/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.1220 - val_accuracy: 0.9805
Epoch 56/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.1303 - val_accuracy: 0.9781
Epoch 57/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.1516 - val_accuracy: 0.9765
Epoch 58/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0059 - accuracy: 0.9981 - val_loss: 0.1347 - val_accuracy: 0.9761
Epoch 59/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0015 - accuracy: 0.9996 - val_loss: 0.1199 - val_accuracy: 0.9823
Epoch 60/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 0.1304 - val_accuracy: 0.9807
Epoch 61/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9985 - val_loss: 0.1187 - val_accuracy: 0.9814
Epoch 62/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.1225 - val_accuracy: 0.9806
Epoch 63/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1288 - val_accuracy: 0.9820
Epoch 64/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.1309 - val_accuracy: 0.9803
Epoch 65/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 0.1178 - val_accuracy: 0.9815
Epoch 66/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.1339 - val_accuracy: 0.9796
Epoch 67/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 0.1222 - val_accuracy: 0.9800
Epoch 68/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 0.1488 - val_accuracy: 0.9775
Epoch 69/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0044 - accuracy: 0.9987 - val_loss: 0.1166 - val_accuracy: 0.9816
Epoch 70/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1232 - val_accuracy: 0.9823
Epoch 71/200
469/469 [==============================] - 2s 3ms/step - loss: 6.5848e-04 - accuracy: 0.9999 - val_loss: 0.1323 - val_accuracy: 0.9793
Epoch 72/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1404 - val_accuracy: 0.9794
Epoch 73/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0068 - accuracy: 0.9981 - val_loss: 0.1481 - val_accuracy: 0.9761
Epoch 74/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0034 - accuracy: 0.9990 - val_loss: 0.1367 - val_accuracy: 0.9791
Epoch 75/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 0.1354 - val_accuracy: 0.9793
Epoch 76/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.1237 - val_accuracy: 0.9814
Epoch 77/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1307 - val_accuracy: 0.9808
Epoch 78/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1322 - val_accuracy: 0.9819
Epoch 79/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.1461 - val_accuracy: 0.9764
Epoch 80/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.1160 - val_accuracy: 0.9820
Epoch 81/200
469/469 [==============================] - 2s 4ms/step - loss: 6.8951e-04 - accuracy: 0.9998 - val_loss: 0.1241 - val_accuracy: 0.9823
Epoch 82/200
469/469 [==============================] - 2s 4ms/step - loss: 8.9305e-04 - accuracy: 0.9998 - val_loss: 0.1241 - val_accuracy: 0.9830
Epoch 83/200
469/469 [==============================] - 2s 3ms/step - loss: 1.8648e-04 - accuracy: 1.0000 - val_loss: 0.1324 - val_accuracy: 0.9826
Epoch 84/200
469/469 [==============================] - 2s 3ms/step - loss: 1.4409e-04 - accuracy: 0.9999 - val_loss: 0.1354 - val_accuracy: 0.9827
Epoch 85/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0025 - accuracy: 0.9994 - val_loss: 0.1421 - val_accuracy: 0.9795
Epoch 86/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0136 - accuracy: 0.9968 - val_loss: 0.1262 - val_accuracy: 0.9809
Epoch 87/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1125 - val_accuracy: 0.9842
Epoch 88/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1248 - val_accuracy: 0.9827
Epoch 89/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0065 - accuracy: 0.9980 - val_loss: 0.1174 - val_accuracy: 0.9802
Epoch 90/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1279 - val_accuracy: 0.9813
Epoch 91/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.1247 - val_accuracy: 0.9825
Epoch 92/200
469/469 [==============================] - 2s 3ms/step - loss: 1.8251e-04 - accuracy: 1.0000 - val_loss: 0.1281 - val_accuracy: 0.9826
Epoch 93/200
469/469 [==============================] - 2s 3ms/step - loss: 2.4101e-04 - accuracy: 1.0000 - val_loss: 0.1292 - val_accuracy: 0.9824
Epoch 94/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2869e-04 - accuracy: 1.0000 - val_loss: 0.1314 - val_accuracy: 0.9831
Epoch 95/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0085 - accuracy: 0.9978 - val_loss: 0.1287 - val_accuracy: 0.9803
Epoch 96/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.1450 - val_accuracy: 0.9777
Epoch 97/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1247 - val_accuracy: 0.9818
Epoch 98/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1317 - val_accuracy: 0.9821
Epoch 99/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1510 - val_accuracy: 0.9801
Epoch 100/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.1840 - val_accuracy: 0.9747
Epoch 101/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.1273 - val_accuracy: 0.9805
Epoch 102/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.1291 - val_accuracy: 0.9816
Epoch 103/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.1629 - val_accuracy: 0.9793
Epoch 104/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1206 - val_accuracy: 0.9832
Epoch 105/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.1239 - val_accuracy: 0.9829
Epoch 106/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.1462 - val_accuracy: 0.9809
Epoch 107/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.1268 - val_accuracy: 0.9814
Epoch 108/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 0.1222 - val_accuracy: 0.9832
Epoch 109/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.1354 - val_accuracy: 0.9807
Epoch 110/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1306 - val_accuracy: 0.9832
Epoch 111/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0030 - accuracy: 0.9993 - val_loss: 0.1226 - val_accuracy: 0.9805
Epoch 112/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1681 - val_accuracy: 0.9723
Epoch 113/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.1334 - val_accuracy: 0.9820
Epoch 114/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1232 - val_accuracy: 0.9818
Epoch 115/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.1342 - val_accuracy: 0.9802
Epoch 116/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.1305 - val_accuracy: 0.9811
Epoch 117/200
469/469 [==============================] - 2s 4ms/step - loss: 3.8401e-04 - accuracy: 0.9999 - val_loss: 0.1292 - val_accuracy: 0.9820
Epoch 118/200
469/469 [==============================] - 2s 4ms/step - loss: 4.3346e-05 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9816
Epoch 119/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7511e-05 - accuracy: 1.0000 - val_loss: 0.1386 - val_accuracy: 0.9818
Epoch 120/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2479e-05 - accuracy: 1.0000 - val_loss: 0.1415 - val_accuracy: 0.9819
Epoch 121/200
469/469 [==============================] - 2s 3ms/step - loss: 9.3661e-06 - accuracy: 1.0000 - val_loss: 0.1443 - val_accuracy: 0.9821
Epoch 122/200
469/469 [==============================] - 2s 3ms/step - loss: 7.3577e-06 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9822
Epoch 123/200
469/469 [==============================] - 2s 3ms/step - loss: 5.7041e-06 - accuracy: 1.0000 - val_loss: 0.1501 - val_accuracy: 0.9822
Epoch 124/200
469/469 [==============================] - 2s 3ms/step - loss: 4.4694e-06 - accuracy: 1.0000 - val_loss: 0.1525 - val_accuracy: 0.9822
Epoch 125/200
469/469 [==============================] - 2s 4ms/step - loss: 3.4864e-06 - accuracy: 1.0000 - val_loss: 0.1552 - val_accuracy: 0.9821
Epoch 126/200
469/469 [==============================] - 2s 4ms/step - loss: 2.8008e-06 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9820
Epoch 127/200
469/469 [==============================] - 2s 3ms/step - loss: 2.2308e-06 - accuracy: 1.0000 - val_loss: 0.1603 - val_accuracy: 0.9820
Epoch 128/200
469/469 [==============================] - 2s 3ms/step - loss: 1.7629e-06 - accuracy: 1.0000 - val_loss: 0.1628 - val_accuracy: 0.9822
Epoch 129/200
469/469 [==============================] - 2s 4ms/step - loss: 1.4402e-06 - accuracy: 1.0000 - val_loss: 0.1647 - val_accuracy: 0.9822
Epoch 130/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1301e-06 - accuracy: 1.0000 - val_loss: 0.1672 - val_accuracy: 0.9822
Epoch 131/200
469/469 [==============================] - 2s 3ms/step - loss: 9.2444e-07 - accuracy: 1.0000 - val_loss: 0.1694 - val_accuracy: 0.9822
Epoch 132/200
469/469 [==============================] - 2s 3ms/step - loss: 7.4471e-07 - accuracy: 1.0000 - val_loss: 0.1718 - val_accuracy: 0.9824
Epoch 133/200
469/469 [==============================] - 2s 3ms/step - loss: 5.8486e-07 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9823
Epoch 134/200
469/469 [==============================] - 2s 3ms/step - loss: 4.6834e-07 - accuracy: 1.0000 - val_loss: 0.1761 - val_accuracy: 0.9822
Epoch 135/200
469/469 [==============================] - 2s 3ms/step - loss: 3.7385e-07 - accuracy: 1.0000 - val_loss: 0.1782 - val_accuracy: 0.9823
Epoch 136/200
469/469 [==============================] - 2s 4ms/step - loss: 3.1184e-07 - accuracy: 1.0000 - val_loss: 0.1801 - val_accuracy: 0.9824
Epoch 137/200
469/469 [==============================] - 2s 4ms/step - loss: 2.5207e-07 - accuracy: 1.0000 - val_loss: 0.1817 - val_accuracy: 0.9823
Epoch 138/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9173e-07 - accuracy: 1.0000 - val_loss: 0.1836 - val_accuracy: 0.9823
Epoch 139/200
469/469 [==============================] - 2s 3ms/step - loss: 1.6292e-07 - accuracy: 1.0000 - val_loss: 0.1852 - val_accuracy: 0.9823
Epoch 140/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3587e-07 - accuracy: 1.0000 - val_loss: 0.1870 - val_accuracy: 0.9823
Epoch 141/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1000e-07 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9823
Epoch 142/200
469/469 [==============================] - 2s 4ms/step - loss: 8.6909e-08 - accuracy: 1.0000 - val_loss: 0.1902 - val_accuracy: 0.9822
Epoch 143/200
469/469 [==============================] - 2s 3ms/step - loss: 6.9925e-08 - accuracy: 1.0000 - val_loss: 0.1916 - val_accuracy: 0.9822
Epoch 144/200
469/469 [==============================] - 2s 4ms/step - loss: 5.9532e-08 - accuracy: 1.0000 - val_loss: 0.1931 - val_accuracy: 0.9822
Epoch 145/200
469/469 [==============================] - 2s 3ms/step - loss: 5.0323e-08 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9822
Epoch 146/200
469/469 [==============================] - 2s 4ms/step - loss: 3.9739e-08 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9822
Epoch 147/200
469/469 [==============================] - 2s 4ms/step - loss: 2.8432e-08 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9823
Epoch 148/200
469/469 [==============================] - 2s 4ms/step - loss: 2.3689e-08 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9824
Epoch 149/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0081e-08 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9824
Epoch 150/200
469/469 [==============================] - 2s 3ms/step - loss: 1.6603e-08 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9824
Epoch 151/200
469/469 [==============================] - 2s 3ms/step - loss: 1.4272e-08 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9824
Epoch 152/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2482e-08 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9824
Epoch 153/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0863e-08 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9824
Epoch 154/200
469/469 [==============================] - 2s 3ms/step - loss: 9.5046e-09 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9824
Epoch 155/200
469/469 [==============================] - 2s 4ms/step - loss: 8.4040e-09 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9824
Epoch 156/200
469/469 [==============================] - 2s 3ms/step - loss: 7.7185e-09 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9824
Epoch 157/200
469/469 [==============================] - 2s 3ms/step - loss: 6.9795e-09 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9824
Epoch 158/200
469/469 [==============================] - 2s 3ms/step - loss: 6.1828e-09 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9824
Epoch 159/200
469/469 [==============================] - 2s 3ms/step - loss: 5.8014e-09 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9824
Epoch 160/200
469/469 [==============================] - 2s 3ms/step - loss: 5.3067e-09 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9825
Epoch 161/200
469/469 [==============================] - 2s 3ms/step - loss: 4.9471e-09 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9825
Epoch 162/200
469/469 [==============================] - 2s 3ms/step - loss: 4.6213e-09 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9825
Epoch 163/200
469/469 [==============================] - 2s 3ms/step - loss: 4.2776e-09 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9825
Epoch 164/200
469/469 [==============================] - 2s 3ms/step - loss: 4.0074e-09 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9825
Epoch 165/200
469/469 [==============================] - 2s 4ms/step - loss: 3.7570e-09 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9825
Epoch 166/200
469/469 [==============================] - 2s 4ms/step - loss: 3.5385e-09 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9825
Epoch 167/200
469/469 [==============================] - 2s 3ms/step - loss: 3.3656e-09 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9825
Epoch 168/200
469/469 [==============================] - 2s 3ms/step - loss: 3.2027e-09 - accuracy: 1.0000 - val_loss: 0.2088 - val_accuracy: 0.9825
Epoch 169/200
469/469 [==============================] - 2s 4ms/step - loss: 3.0497e-09 - accuracy: 1.0000 - val_loss: 0.2091 - val_accuracy: 0.9825
Epoch 170/200
469/469 [==============================] - 2s 4ms/step - loss: 2.8809e-09 - accuracy: 1.0000 - val_loss: 0.2094 - val_accuracy: 0.9825
Epoch 171/200
469/469 [==============================] - 2s 4ms/step - loss: 2.7557e-09 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9825
Epoch 172/200
469/469 [==============================] - 2s 4ms/step - loss: 2.6504e-09 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9825
Epoch 173/200
469/469 [==============================] - 2s 4ms/step - loss: 2.5411e-09 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.9825
Epoch 174/200
469/469 [==============================] - 2s 4ms/step - loss: 2.4537e-09 - accuracy: 1.0000 - val_loss: 0.2105 - val_accuracy: 0.9826
Epoch 175/200
469/469 [==============================] - 2s 4ms/step - loss: 2.3345e-09 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9826
Epoch 176/200
469/469 [==============================] - 2s 4ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2110 - val_accuracy: 0.9827
Epoch 177/200
469/469 [==============================] - 2s 4ms/step - loss: 2.1716e-09 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9827
Epoch 178/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0722e-09 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9827
Epoch 179/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0206e-09 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9827
Epoch 180/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9431e-09 - accuracy: 1.0000 - val_loss: 0.2118 - val_accuracy: 0.9827
Epoch 181/200
469/469 [==============================] - 2s 4ms/step - loss: 1.8835e-09 - accuracy: 1.0000 - val_loss: 0.2120 - val_accuracy: 0.9827
Epoch 182/200
469/469 [==============================] - 2s 3ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9827
Epoch 183/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7722e-09 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9827
Epoch 184/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7385e-09 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.9827
Epoch 185/200
469/469 [==============================] - 2s 4ms/step - loss: 1.6848e-09 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9827
Epoch 186/200
469/469 [==============================] - 2s 3ms/step - loss: 1.6510e-09 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9827
Epoch 187/200
469/469 [==============================] - 2s 4ms/step - loss: 1.6014e-09 - accuracy: 1.0000 - val_loss: 0.2131 - val_accuracy: 0.9827
Epoch 188/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-09 - accuracy: 1.0000 - val_loss: 0.2133 - val_accuracy: 0.9827
Epoch 189/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5199e-09 - accuracy: 1.0000 - val_loss: 0.2134 - val_accuracy: 0.9827
Epoch 190/200
469/469 [==============================] - 2s 3ms/step - loss: 1.4802e-09 - accuracy: 1.0000 - val_loss: 0.2136 - val_accuracy: 0.9827
Epoch 191/200
469/469 [==============================] - 2s 4ms/step - loss: 1.4424e-09 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9827
Epoch 192/200
469/469 [==============================] - 2s 3ms/step - loss: 1.4047e-09 - accuracy: 1.0000 - val_loss: 0.2139 - val_accuracy: 0.9828
Epoch 193/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3709e-09 - accuracy: 1.0000 - val_loss: 0.2141 - val_accuracy: 0.9828
Epoch 194/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3252e-09 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9828
Epoch 195/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2974e-09 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9828
Epoch 196/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2835e-09 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9828
Epoch 197/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2418e-09 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9828
Epoch 198/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2239e-09 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9828
Epoch 199/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2080e-09 - accuracy: 1.0000 - val_loss: 0.2148 - val_accuracy: 0.9828
Epoch 200/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1722e-09 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9828

(b) Activation function: the logistic sigmoid function; initialization: Xavier initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        #tf.keras.layers.Dense(512,kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_4 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_24 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_25 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_26 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_27 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_28 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_29 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam() #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
historya1 = model.fit(X_train, y_train, batch_size=128, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
469/469 [==============================] - 2s 4ms/step - loss: 0.8131 - accuracy: 0.7149 - val_loss: 0.3044 - val_accuracy: 0.9094
Epoch 2/200
469/469 [==============================] - 2s 3ms/step - loss: 0.2390 - accuracy: 0.9298 - val_loss: 0.1955 - val_accuracy: 0.9406
Epoch 3/200
469/469 [==============================] - 2s 3ms/step - loss: 0.1628 - accuracy: 0.9516 - val_loss: 0.1500 - val_accuracy: 0.9555
Epoch 4/200
469/469 [==============================] - 2s 4ms/step - loss: 0.1233 - accuracy: 0.9635 - val_loss: 0.1188 - val_accuracy: 0.9632
Epoch 5/200
469/469 [==============================] - 2s 4ms/step - loss: 0.1013 - accuracy: 0.9692 - val_loss: 0.1172 - val_accuracy: 0.9663
Epoch 6/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0859 - accuracy: 0.9740 - val_loss: 0.1048 - val_accuracy: 0.9698
Epoch 7/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0694 - accuracy: 0.9795 - val_loss: 0.0946 - val_accuracy: 0.9743
Epoch 8/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0593 - accuracy: 0.9819 - val_loss: 0.0973 - val_accuracy: 0.9715
Epoch 9/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0506 - accuracy: 0.9850 - val_loss: 0.0898 - val_accuracy: 0.9745
Epoch 10/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0453 - accuracy: 0.9863 - val_loss: 0.0797 - val_accuracy: 0.9783
Epoch 11/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0381 - accuracy: 0.9887 - val_loss: 0.0824 - val_accuracy: 0.9777
Epoch 12/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0323 - accuracy: 0.9905 - val_loss: 0.0952 - val_accuracy: 0.9770
Epoch 13/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0314 - accuracy: 0.9908 - val_loss: 0.0864 - val_accuracy: 0.9777
Epoch 14/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0251 - accuracy: 0.9919 - val_loss: 0.0930 - val_accuracy: 0.9754
Epoch 15/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0255 - accuracy: 0.9921 - val_loss: 0.0851 - val_accuracy: 0.9795
Epoch 16/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0201 - accuracy: 0.9937 - val_loss: 0.0877 - val_accuracy: 0.9782
Epoch 17/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0179 - accuracy: 0.9946 - val_loss: 0.0958 - val_accuracy: 0.9803
Epoch 18/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0173 - accuracy: 0.9947 - val_loss: 0.0897 - val_accuracy: 0.9795
Epoch 19/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.0908 - val_accuracy: 0.9793
Epoch 20/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9952 - val_loss: 0.1051 - val_accuracy: 0.9779
Epoch 21/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0159 - accuracy: 0.9951 - val_loss: 0.0798 - val_accuracy: 0.9816
Epoch 22/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9967 - val_loss: 0.0909 - val_accuracy: 0.9789
Epoch 23/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.1012 - val_accuracy: 0.9795
Epoch 24/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0869 - val_accuracy: 0.9827
Epoch 25/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0106 - accuracy: 0.9969 - val_loss: 0.1158 - val_accuracy: 0.9773
Epoch 26/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9972 - val_loss: 0.1081 - val_accuracy: 0.9808
Epoch 27/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0099 - accuracy: 0.9971 - val_loss: 0.0969 - val_accuracy: 0.9809
Epoch 28/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.1194 - val_accuracy: 0.9783
Epoch 29/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0080 - accuracy: 0.9975 - val_loss: 0.0938 - val_accuracy: 0.9831
Epoch 30/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.1113 - val_accuracy: 0.9808
Epoch 31/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.1206 - val_accuracy: 0.9790
Epoch 32/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0068 - accuracy: 0.9980 - val_loss: 0.1048 - val_accuracy: 0.9812
Epoch 33/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.1038 - val_accuracy: 0.9824
Epoch 34/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0073 - accuracy: 0.9978 - val_loss: 0.0916 - val_accuracy: 0.9829
Epoch 35/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 0.0897 - val_accuracy: 0.9834
Epoch 36/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0072 - accuracy: 0.9977 - val_loss: 0.0945 - val_accuracy: 0.9819
Epoch 37/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9982 - val_loss: 0.1134 - val_accuracy: 0.9816
Epoch 38/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9984 - val_loss: 0.1114 - val_accuracy: 0.9818
Epoch 39/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0066 - accuracy: 0.9979 - val_loss: 0.1002 - val_accuracy: 0.9820
Epoch 40/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0064 - accuracy: 0.9981 - val_loss: 0.1066 - val_accuracy: 0.9802
Epoch 41/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.1112 - val_accuracy: 0.9803
Epoch 42/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.1058 - val_accuracy: 0.9829
Epoch 43/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.1122 - val_accuracy: 0.9833
Epoch 44/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.1446 - val_accuracy: 0.9802
Epoch 45/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.1108 - val_accuracy: 0.9818
Epoch 46/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 0.1388 - val_accuracy: 0.9789
Epoch 47/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1019 - val_accuracy: 0.9818
Epoch 48/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.1167 - val_accuracy: 0.9830
Epoch 49/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0026 - accuracy: 0.9991 - val_loss: 0.1184 - val_accuracy: 0.9821
Epoch 50/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.1055 - val_accuracy: 0.9842
Epoch 51/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1385 - val_accuracy: 0.9770
Epoch 52/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.1125 - val_accuracy: 0.9823
Epoch 53/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.1224 - val_accuracy: 0.9837
Epoch 54/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0062 - accuracy: 0.9984 - val_loss: 0.1171 - val_accuracy: 0.9831
Epoch 55/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0045 - accuracy: 0.9987 - val_loss: 0.1130 - val_accuracy: 0.9830
Epoch 56/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.1011 - val_accuracy: 0.9838
Epoch 57/200
469/469 [==============================] - 2s 4ms/step - loss: 9.1745e-04 - accuracy: 0.9997 - val_loss: 0.1178 - val_accuracy: 0.9837
Epoch 58/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.1208 - val_accuracy: 0.9815
Epoch 59/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0058 - accuracy: 0.9985 - val_loss: 0.0981 - val_accuracy: 0.9837
Epoch 60/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0027 - accuracy: 0.9991 - val_loss: 0.1092 - val_accuracy: 0.9845
Epoch 61/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.1207 - val_accuracy: 0.9823
Epoch 62/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1155 - val_accuracy: 0.9844
Epoch 63/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2653e-04 - accuracy: 1.0000 - val_loss: 0.1228 - val_accuracy: 0.9843
Epoch 64/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 0.1367 - val_accuracy: 0.9791
Epoch 65/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.1177 - val_accuracy: 0.9829
Epoch 66/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0038 - accuracy: 0.9990 - val_loss: 0.1024 - val_accuracy: 0.9844
Epoch 67/200
469/469 [==============================] - 2s 3ms/step - loss: 4.1127e-04 - accuracy: 0.9999 - val_loss: 0.1101 - val_accuracy: 0.9847
Epoch 68/200
469/469 [==============================] - 2s 3ms/step - loss: 5.6425e-05 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9852
Epoch 69/200
469/469 [==============================] - 2s 3ms/step - loss: 2.1659e-05 - accuracy: 1.0000 - val_loss: 0.1184 - val_accuracy: 0.9855
Epoch 70/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0396e-05 - accuracy: 1.0000 - val_loss: 0.1226 - val_accuracy: 0.9854
Epoch 71/200
469/469 [==============================] - 2s 4ms/step - loss: 7.1113e-06 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9854
Epoch 72/200
469/469 [==============================] - 2s 3ms/step - loss: 4.9408e-06 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9852
Epoch 73/200
469/469 [==============================] - 2s 4ms/step - loss: 3.5696e-06 - accuracy: 1.0000 - val_loss: 0.1331 - val_accuracy: 0.9851
Epoch 74/200
469/469 [==============================] - 2s 4ms/step - loss: 2.6280e-06 - accuracy: 1.0000 - val_loss: 0.1361 - val_accuracy: 0.9851
Epoch 75/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0153e-06 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9852
Epoch 76/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5774e-06 - accuracy: 1.0000 - val_loss: 0.1419 - val_accuracy: 0.9850
Epoch 77/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2338e-06 - accuracy: 1.0000 - val_loss: 0.1446 - val_accuracy: 0.9849
Epoch 78/200
469/469 [==============================] - 2s 3ms/step - loss: 9.7848e-07 - accuracy: 1.0000 - val_loss: 0.1473 - val_accuracy: 0.9849
Epoch 79/200
469/469 [==============================] - 2s 3ms/step - loss: 7.7092e-07 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9849
Epoch 80/200
469/469 [==============================] - 2s 4ms/step - loss: 6.2419e-07 - accuracy: 1.0000 - val_loss: 0.1523 - val_accuracy: 0.9850
Epoch 81/200
469/469 [==============================] - 2s 3ms/step - loss: 4.9624e-07 - accuracy: 1.0000 - val_loss: 0.1549 - val_accuracy: 0.9849
Epoch 82/200
469/469 [==============================] - 2s 4ms/step - loss: 3.9337e-07 - accuracy: 1.0000 - val_loss: 0.1571 - val_accuracy: 0.9849
Epoch 83/200
469/469 [==============================] - 2s 3ms/step - loss: 3.1382e-07 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9849
Epoch 84/200
469/469 [==============================] - 2s 3ms/step - loss: 2.4437e-07 - accuracy: 1.0000 - val_loss: 0.1616 - val_accuracy: 0.9849
Epoch 85/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9419e-07 - accuracy: 1.0000 - val_loss: 0.1637 - val_accuracy: 0.9849
Epoch 86/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5399e-07 - accuracy: 1.0000 - val_loss: 0.1656 - val_accuracy: 0.9850
Epoch 87/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2316e-07 - accuracy: 1.0000 - val_loss: 0.1674 - val_accuracy: 0.9851
Epoch 88/200
469/469 [==============================] - 2s 3ms/step - loss: 9.9206e-08 - accuracy: 1.0000 - val_loss: 0.1691 - val_accuracy: 0.9852
Epoch 89/200
469/469 [==============================] - 2s 3ms/step - loss: 8.0772e-08 - accuracy: 1.0000 - val_loss: 0.1708 - val_accuracy: 0.9853
Epoch 90/200
469/469 [==============================] - 2s 3ms/step - loss: 6.6904e-08 - accuracy: 1.0000 - val_loss: 0.1725 - val_accuracy: 0.9853
Epoch 91/200
469/469 [==============================] - 2s 3ms/step - loss: 5.5542e-08 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9853
Epoch 92/200
469/469 [==============================] - 2s 3ms/step - loss: 4.5446e-08 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9852
Epoch 93/200
469/469 [==============================] - 2s 4ms/step - loss: 3.8587e-08 - accuracy: 1.0000 - val_loss: 0.1770 - val_accuracy: 0.9853
Epoch 94/200
469/469 [==============================] - 2s 4ms/step - loss: 3.2603e-08 - accuracy: 1.0000 - val_loss: 0.1784 - val_accuracy: 0.9853
Epoch 95/200
469/469 [==============================] - 2s 4ms/step - loss: 2.7673e-08 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9853
Epoch 96/200
469/469 [==============================] - 2s 4ms/step - loss: 2.3509e-08 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9853
Epoch 97/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0257e-08 - accuracy: 1.0000 - val_loss: 0.1821 - val_accuracy: 0.9853
Epoch 98/200
469/469 [==============================] - 2s 3ms/step - loss: 1.7350e-08 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9853
Epoch 99/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5109e-08 - accuracy: 1.0000 - val_loss: 0.1843 - val_accuracy: 0.9853
Epoch 100/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3103e-08 - accuracy: 1.0000 - val_loss: 0.1854 - val_accuracy: 0.9854
Epoch 101/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1583e-08 - accuracy: 1.0000 - val_loss: 0.1863 - val_accuracy: 0.9856
Epoch 102/200
469/469 [==============================] - 2s 4ms/step - loss: 1.0198e-08 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9856
Epoch 103/200
469/469 [==============================] - 2s 4ms/step - loss: 8.9962e-09 - accuracy: 1.0000 - val_loss: 0.1882 - val_accuracy: 0.9854
Epoch 104/200
469/469 [==============================] - 2s 4ms/step - loss: 8.0803e-09 - accuracy: 1.0000 - val_loss: 0.1890 - val_accuracy: 0.9854
Epoch 105/200
469/469 [==============================] - 2s 4ms/step - loss: 7.2180e-09 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9855
Epoch 106/200
469/469 [==============================] - 2s 4ms/step - loss: 6.5008e-09 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9856
Epoch 107/200
469/469 [==============================] - 2s 4ms/step - loss: 5.9346e-09 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9856
Epoch 108/200
469/469 [==============================] - 2s 4ms/step - loss: 5.3544e-09 - accuracy: 1.0000 - val_loss: 0.1920 - val_accuracy: 0.9856
Epoch 109/200
469/469 [==============================] - 2s 3ms/step - loss: 4.9054e-09 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9856
Epoch 110/200
469/469 [==============================] - 2s 3ms/step - loss: 4.4783e-09 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9856
Epoch 111/200
469/469 [==============================] - 2s 3ms/step - loss: 4.1524e-09 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9856
Epoch 112/200
469/469 [==============================] - 2s 3ms/step - loss: 3.8842e-09 - accuracy: 1.0000 - val_loss: 0.1943 - val_accuracy: 0.9855
Epoch 113/200
469/469 [==============================] - 2s 3ms/step - loss: 3.6080e-09 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9855
Epoch 114/200
469/469 [==============================] - 2s 4ms/step - loss: 3.3418e-09 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9855
Epoch 115/200
469/469 [==============================] - 2s 4ms/step - loss: 3.1094e-09 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9855
Epoch 116/200
469/469 [==============================] - 2s 4ms/step - loss: 2.9445e-09 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9854
Epoch 117/200
469/469 [==============================] - 2s 4ms/step - loss: 2.7756e-09 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9854
Epoch 118/200
469/469 [==============================] - 2s 4ms/step - loss: 2.6425e-09 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9853
Epoch 119/200
469/469 [==============================] - 2s 4ms/step - loss: 2.5074e-09 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9853
Epoch 120/200
469/469 [==============================] - 2s 4ms/step - loss: 2.3583e-09 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9853
Epoch 121/200
469/469 [==============================] - 2s 4ms/step - loss: 2.2471e-09 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9853
Epoch 122/200
469/469 [==============================] - 2s 4ms/step - loss: 2.1696e-09 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9853
Epoch 123/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0643e-09 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9853
Epoch 124/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9888e-09 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9853
Epoch 125/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9073e-09 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9853
Epoch 126/200
469/469 [==============================] - 2s 3ms/step - loss: 1.8299e-09 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9853
Epoch 127/200
469/469 [==============================] - 2s 3ms/step - loss: 1.7563e-09 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9853
Epoch 128/200
469/469 [==============================] - 2s 3ms/step - loss: 1.7027e-09 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9853
Epoch 129/200
469/469 [==============================] - 2s 3ms/step - loss: 1.6491e-09 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9852
Epoch 130/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5875e-09 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9852
Epoch 131/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5517e-09 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9852
Epoch 132/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5100e-09 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9852
Epoch 133/200
469/469 [==============================] - 2s 4ms/step - loss: 1.4603e-09 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9852
Epoch 134/200
469/469 [==============================] - 2s 4ms/step - loss: 1.4226e-09 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9852
Epoch 135/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3669e-09 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9852
Epoch 136/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3351e-09 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9852
Epoch 137/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2994e-09 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9852
Epoch 138/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2557e-09 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9851
Epoch 139/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2279e-09 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9851
Epoch 140/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1901e-09 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9851
Epoch 141/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1702e-09 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9851
Epoch 142/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1265e-09 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9851
Epoch 143/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0908e-09 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9851
Epoch 144/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0729e-09 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9851
Epoch 145/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0471e-09 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9851
Epoch 146/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0212e-09 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9851
Epoch 147/200
469/469 [==============================] - 2s 3ms/step - loss: 9.8546e-10 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9851
Epoch 148/200
469/469 [==============================] - 2s 3ms/step - loss: 9.6758e-10 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9851
Epoch 149/200
469/469 [==============================] - 2s 3ms/step - loss: 9.5367e-10 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9851
Epoch 150/200
469/469 [==============================] - 2s 3ms/step - loss: 9.2983e-10 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9851
Epoch 151/200
469/469 [==============================] - 2s 4ms/step - loss: 9.1394e-10 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9851
Epoch 152/200
469/469 [==============================] - 2s 3ms/step - loss: 9.0202e-10 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9851
Epoch 153/200
469/469 [==============================] - 2s 4ms/step - loss: 8.8811e-10 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9851
Epoch 154/200
469/469 [==============================] - 2s 3ms/step - loss: 8.7023e-10 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9851
Epoch 155/200
469/469 [==============================] - 2s 3ms/step - loss: 8.5632e-10 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9851
Epoch 156/200
469/469 [==============================] - 2s 4ms/step - loss: 8.3446e-10 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9851
Epoch 157/200
469/469 [==============================] - 2s 3ms/step - loss: 8.1460e-10 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9851
Epoch 158/200
469/469 [==============================] - 2s 4ms/step - loss: 8.0665e-10 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9851
Epoch 159/200
469/469 [==============================] - 2s 3ms/step - loss: 7.9075e-10 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9851
Epoch 160/200
469/469 [==============================] - 2s 3ms/step - loss: 7.8479e-10 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9851
Epoch 161/200
469/469 [==============================] - 2s 3ms/step - loss: 7.6691e-10 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9851
Epoch 162/200
469/469 [==============================] - 2s 3ms/step - loss: 7.5102e-10 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9851
Epoch 163/200
469/469 [==============================] - 2s 4ms/step - loss: 7.3711e-10 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9851
Epoch 164/200
469/469 [==============================] - 2s 4ms/step - loss: 7.2916e-10 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9851
Epoch 165/200
469/469 [==============================] - 2s 4ms/step - loss: 7.0929e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9851
Epoch 166/200
469/469 [==============================] - 2s 3ms/step - loss: 6.9141e-10 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9851
Epoch 167/200
469/469 [==============================] - 2s 4ms/step - loss: 6.8347e-10 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9851
Epoch 168/200
469/469 [==============================] - 2s 4ms/step - loss: 6.6956e-10 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9851
Epoch 169/200
469/469 [==============================] - 2s 4ms/step - loss: 6.6161e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9851
Epoch 170/200
469/469 [==============================] - 2s 4ms/step - loss: 6.4969e-10 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9851
Epoch 171/200
469/469 [==============================] - 2s 4ms/step - loss: 6.3380e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9851
Epoch 172/200
469/469 [==============================] - 2s 3ms/step - loss: 6.2585e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9851
Epoch 173/200
469/469 [==============================] - 2s 3ms/step - loss: 6.1591e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9851
Epoch 174/200
469/469 [==============================] - 2s 4ms/step - loss: 6.0797e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9851
Epoch 175/200
469/469 [==============================] - 2s 4ms/step - loss: 6.0399e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9851
Epoch 176/200
469/469 [==============================] - 2s 3ms/step - loss: 5.8611e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9851
Epoch 177/200
469/469 [==============================] - 2s 3ms/step - loss: 5.8214e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9851
Epoch 178/200
469/469 [==============================] - 2s 3ms/step - loss: 5.7022e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9851
Epoch 179/200
469/469 [==============================] - 2s 3ms/step - loss: 5.7022e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9851
Epoch 180/200
469/469 [==============================] - 2s 3ms/step - loss: 5.6426e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9851
Epoch 181/200
469/469 [==============================] - 2s 3ms/step - loss: 5.5035e-10 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9851
Epoch 182/200
469/469 [==============================] - 2s 3ms/step - loss: 5.4240e-10 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9851
Epoch 183/200
469/469 [==============================] - 2s 3ms/step - loss: 5.3644e-10 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9851
Epoch 184/200
469/469 [==============================] - 2s 4ms/step - loss: 5.3247e-10 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9851
Epoch 185/200
469/469 [==============================] - 2s 3ms/step - loss: 5.2253e-10 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9851
Epoch 186/200
469/469 [==============================] - 2s 3ms/step - loss: 5.1657e-10 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9851
Epoch 187/200
469/469 [==============================] - 2s 4ms/step - loss: 5.0863e-10 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9851
Epoch 188/200
469/469 [==============================] - 2s 3ms/step - loss: 5.0068e-10 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9851
Epoch 189/200
469/469 [==============================] - 2s 4ms/step - loss: 4.9670e-10 - accuracy: 1.0000 - val_loss: 0.2078 - val_accuracy: 0.9851
Epoch 190/200
469/469 [==============================] - 2s 4ms/step - loss: 4.9273e-10 - accuracy: 1.0000 - val_loss: 0.2079 - val_accuracy: 0.9851
Epoch 191/200
469/469 [==============================] - 2s 3ms/step - loss: 4.8478e-10 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9851
Epoch 192/200
469/469 [==============================] - 2s 3ms/step - loss: 4.8081e-10 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9851
Epoch 193/200
469/469 [==============================] - 2s 3ms/step - loss: 4.6889e-10 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9851
Epoch 194/200
469/469 [==============================] - 2s 4ms/step - loss: 4.6293e-10 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9851
Epoch 195/200
469/469 [==============================] - 2s 4ms/step - loss: 4.5299e-10 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9851
Epoch 196/200
469/469 [==============================] - 2s 3ms/step - loss: 4.4902e-10 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9851
Epoch 197/200
469/469 [==============================] - 2s 4ms/step - loss: 4.4306e-10 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9852
Epoch 198/200
469/469 [==============================] - 2s 4ms/step - loss: 4.4306e-10 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9852
Epoch 199/200
469/469 [==============================] - 2s 4ms/step - loss: 4.3313e-10 - accuracy: 1.0000 - val_loss: 0.2084 - val_accuracy: 0.9852
Epoch 200/200
469/469 [==============================] - 2s 4ms/step - loss: 4.3114e-10 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9852

(c) Activation function: ReLU; initialization: random numbers generated from the normal

distribution (μ = 0, σ = 0.01)

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        #tf.keras.layers.Dense(512,kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_5 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_30 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_31 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_32 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_33 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_34 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_35 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam() #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
historya2 = model.fit(X_train, y_train, batch_size=128, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
469/469 [==============================] - 2s 4ms/step - loss: 0.5278 - accuracy: 0.8253 - val_loss: 0.1801 - val_accuracy: 0.9498
Epoch 2/200
469/469 [==============================] - 2s 4ms/step - loss: 0.1554 - accuracy: 0.9572 - val_loss: 0.1551 - val_accuracy: 0.9558
Epoch 3/200
469/469 [==============================] - 2s 3ms/step - loss: 0.1022 - accuracy: 0.9715 - val_loss: 0.1108 - val_accuracy: 0.9672
Epoch 4/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0734 - accuracy: 0.9793 - val_loss: 0.0868 - val_accuracy: 0.9764
Epoch 5/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0590 - accuracy: 0.9827 - val_loss: 0.0990 - val_accuracy: 0.9731
Epoch 6/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0470 - accuracy: 0.9862 - val_loss: 0.1011 - val_accuracy: 0.9734
Epoch 7/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0438 - accuracy: 0.9873 - val_loss: 0.0937 - val_accuracy: 0.9772
Epoch 8/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0351 - accuracy: 0.9903 - val_loss: 0.0861 - val_accuracy: 0.9793
Epoch 9/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0320 - accuracy: 0.9905 - val_loss: 0.0939 - val_accuracy: 0.9769
Epoch 10/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0289 - accuracy: 0.9913 - val_loss: 0.0830 - val_accuracy: 0.9800
Epoch 11/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0235 - accuracy: 0.9930 - val_loss: 0.0899 - val_accuracy: 0.9802
Epoch 12/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0249 - accuracy: 0.9930 - val_loss: 0.1014 - val_accuracy: 0.9782
Epoch 13/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0198 - accuracy: 0.9944 - val_loss: 0.0918 - val_accuracy: 0.9796
Epoch 14/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0182 - accuracy: 0.9949 - val_loss: 0.0872 - val_accuracy: 0.9813
Epoch 15/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0207 - accuracy: 0.9939 - val_loss: 0.0792 - val_accuracy: 0.9824
Epoch 16/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9961 - val_loss: 0.1060 - val_accuracy: 0.9814
Epoch 17/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0183 - accuracy: 0.9953 - val_loss: 0.1247 - val_accuracy: 0.9768
Epoch 18/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9959 - val_loss: 0.1050 - val_accuracy: 0.9797
Epoch 19/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.1076 - val_accuracy: 0.9803
Epoch 20/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9966 - val_loss: 0.0978 - val_accuracy: 0.9826
Epoch 21/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.1301 - val_accuracy: 0.9801
Epoch 22/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9969 - val_loss: 0.1251 - val_accuracy: 0.9797
Epoch 23/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0148 - accuracy: 0.9960 - val_loss: 0.1114 - val_accuracy: 0.9808
Epoch 24/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0093 - accuracy: 0.9974 - val_loss: 0.0999 - val_accuracy: 0.9823
Epoch 25/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9967 - val_loss: 0.1153 - val_accuracy: 0.9833
Epoch 26/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9970 - val_loss: 0.1006 - val_accuracy: 0.9829
Epoch 27/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0095 - accuracy: 0.9975 - val_loss: 0.1098 - val_accuracy: 0.9837
Epoch 28/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0103 - accuracy: 0.9973 - val_loss: 0.1225 - val_accuracy: 0.9818
Epoch 29/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0128 - accuracy: 0.9969 - val_loss: 0.1125 - val_accuracy: 0.9800
Epoch 30/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0081 - accuracy: 0.9977 - val_loss: 0.1246 - val_accuracy: 0.9827
Epoch 31/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0059 - accuracy: 0.9985 - val_loss: 0.1051 - val_accuracy: 0.9823
Epoch 32/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0079 - accuracy: 0.9981 - val_loss: 0.1333 - val_accuracy: 0.9805
Epoch 33/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9974 - val_loss: 0.1216 - val_accuracy: 0.9819
Epoch 34/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0079 - accuracy: 0.9981 - val_loss: 0.1232 - val_accuracy: 0.9811
Epoch 35/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0102 - accuracy: 0.9974 - val_loss: 0.1330 - val_accuracy: 0.9829
Epoch 36/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0087 - accuracy: 0.9980 - val_loss: 0.1052 - val_accuracy: 0.9824
Epoch 37/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.1129 - val_accuracy: 0.9860
Epoch 38/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.1258 - val_accuracy: 0.9838
Epoch 39/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0110 - accuracy: 0.9978 - val_loss: 0.1168 - val_accuracy: 0.9816
Epoch 40/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0106 - accuracy: 0.9973 - val_loss: 0.1050 - val_accuracy: 0.9865
Epoch 41/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0080 - accuracy: 0.9981 - val_loss: 0.1016 - val_accuracy: 0.9845
Epoch 42/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0076 - accuracy: 0.9981 - val_loss: 0.1251 - val_accuracy: 0.9806
Epoch 43/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9979 - val_loss: 0.1034 - val_accuracy: 0.9838
Epoch 44/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0064 - accuracy: 0.9981 - val_loss: 0.1102 - val_accuracy: 0.9836
Epoch 45/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0077 - accuracy: 0.9982 - val_loss: 0.1574 - val_accuracy: 0.9819
Epoch 46/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 0.1013 - val_accuracy: 0.9835
Epoch 47/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.1262 - val_accuracy: 0.9831
Epoch 48/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1280 - val_accuracy: 0.9858
Epoch 49/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 0.1500 - val_accuracy: 0.9839
Epoch 50/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 0.1113 - val_accuracy: 0.9843
Epoch 51/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.1256 - val_accuracy: 0.9835
Epoch 52/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0062 - accuracy: 0.9988 - val_loss: 0.1755 - val_accuracy: 0.9819
Epoch 53/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 0.0993 - val_accuracy: 0.9847
Epoch 54/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1082 - val_accuracy: 0.9849
Epoch 55/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 0.1262 - val_accuracy: 0.9820
Epoch 56/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9987 - val_loss: 0.1302 - val_accuracy: 0.9846
Epoch 57/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0052 - accuracy: 0.9989 - val_loss: 0.2304 - val_accuracy: 0.9826
Epoch 58/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0082 - accuracy: 0.9982 - val_loss: 0.1203 - val_accuracy: 0.9845
Epoch 59/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0052 - accuracy: 0.9989 - val_loss: 0.1245 - val_accuracy: 0.9834
Epoch 60/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0045 - accuracy: 0.9992 - val_loss: 0.1285 - val_accuracy: 0.9855
Epoch 61/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.1615 - val_accuracy: 0.9840
Epoch 62/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0076 - accuracy: 0.9981 - val_loss: 0.1259 - val_accuracy: 0.9809
Epoch 63/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0062 - accuracy: 0.9987 - val_loss: 0.1147 - val_accuracy: 0.9837
Epoch 64/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.1436 - val_accuracy: 0.9837
Epoch 65/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0033 - accuracy: 0.9994 - val_loss: 0.1407 - val_accuracy: 0.9833
Epoch 66/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0057 - accuracy: 0.9987 - val_loss: 0.1416 - val_accuracy: 0.9840
Epoch 67/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0077 - accuracy: 0.9983 - val_loss: 0.1277 - val_accuracy: 0.9836
Epoch 68/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.1467 - val_accuracy: 0.9824
Epoch 69/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.1756 - val_accuracy: 0.9821
Epoch 70/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1143 - val_accuracy: 0.9849
Epoch 71/200
469/469 [==============================] - 2s 4ms/step - loss: 5.5770e-04 - accuracy: 0.9999 - val_loss: 0.1566 - val_accuracy: 0.9833
Epoch 72/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.1597 - val_accuracy: 0.9826
Epoch 73/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0093 - accuracy: 0.9980 - val_loss: 0.1372 - val_accuracy: 0.9838
Epoch 74/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0070 - accuracy: 0.9987 - val_loss: 0.1707 - val_accuracy: 0.9810
Epoch 75/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 0.1386 - val_accuracy: 0.9829
Epoch 76/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0042 - accuracy: 0.9993 - val_loss: 0.1187 - val_accuracy: 0.9834
Epoch 77/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9994 - val_loss: 0.1860 - val_accuracy: 0.9810
Epoch 78/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0046 - accuracy: 0.9991 - val_loss: 0.1719 - val_accuracy: 0.9838
Epoch 79/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0055 - accuracy: 0.9991 - val_loss: 0.1552 - val_accuracy: 0.9860
Epoch 80/200
469/469 [==============================] - 2s 4ms/step - loss: 4.1390e-04 - accuracy: 0.9999 - val_loss: 0.1969 - val_accuracy: 0.9855
Epoch 81/200
469/469 [==============================] - 2s 3ms/step - loss: 5.9587e-06 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9859
Epoch 82/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2535e-06 - accuracy: 1.0000 - val_loss: 0.2221 - val_accuracy: 0.9859
Epoch 83/200
469/469 [==============================] - 2s 3ms/step - loss: 5.5130e-07 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9858
Epoch 84/200
469/469 [==============================] - 2s 3ms/step - loss: 2.7830e-07 - accuracy: 1.0000 - val_loss: 0.2480 - val_accuracy: 0.9858
Epoch 85/200
469/469 [==============================] - 2s 4ms/step - loss: 1.6186e-07 - accuracy: 1.0000 - val_loss: 0.2579 - val_accuracy: 0.9859
Epoch 86/200
469/469 [==============================] - 2s 3ms/step - loss: 1.0094e-07 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9860
Epoch 87/200
469/469 [==============================] - 2s 3ms/step - loss: 6.5931e-08 - accuracy: 1.0000 - val_loss: 0.2749 - val_accuracy: 0.9860
Epoch 88/200
469/469 [==============================] - 2s 4ms/step - loss: 4.4614e-08 - accuracy: 1.0000 - val_loss: 0.2820 - val_accuracy: 0.9860
Epoch 89/200
469/469 [==============================] - 2s 4ms/step - loss: 3.0950e-08 - accuracy: 1.0000 - val_loss: 0.2892 - val_accuracy: 0.9860
Epoch 90/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0931e-08 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9861
Epoch 91/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5086e-08 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9861
Epoch 92/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1146e-08 - accuracy: 1.0000 - val_loss: 0.3054 - val_accuracy: 0.9860
Epoch 93/200
469/469 [==============================] - 2s 4ms/step - loss: 8.4359e-09 - accuracy: 1.0000 - val_loss: 0.3099 - val_accuracy: 0.9860
Epoch 94/200
469/469 [==============================] - 2s 4ms/step - loss: 6.4889e-09 - accuracy: 1.0000 - val_loss: 0.3142 - val_accuracy: 0.9860
Epoch 95/200
469/469 [==============================] - 2s 3ms/step - loss: 5.0485e-09 - accuracy: 1.0000 - val_loss: 0.3183 - val_accuracy: 0.9859
Epoch 96/200
469/469 [==============================] - 2s 4ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.3223 - val_accuracy: 0.9859
Epoch 97/200
469/469 [==============================] - 2s 3ms/step - loss: 3.0915e-09 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9859
Epoch 98/200
469/469 [==============================] - 2s 4ms/step - loss: 2.4160e-09 - accuracy: 1.0000 - val_loss: 0.3298 - val_accuracy: 0.9860
Epoch 99/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9193e-09 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 0.9860
Epoch 100/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5438e-09 - accuracy: 1.0000 - val_loss: 0.3362 - val_accuracy: 0.9859
Epoch 101/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2338e-09 - accuracy: 1.0000 - val_loss: 0.3391 - val_accuracy: 0.9859
Epoch 102/200
469/469 [==============================] - 2s 3ms/step - loss: 9.8546e-10 - accuracy: 1.0000 - val_loss: 0.3420 - val_accuracy: 0.9859
Epoch 103/200
469/469 [==============================] - 2s 3ms/step - loss: 8.2652e-10 - accuracy: 1.0000 - val_loss: 0.3446 - val_accuracy: 0.9859
Epoch 104/200
469/469 [==============================] - 2s 4ms/step - loss: 6.9539e-10 - accuracy: 1.0000 - val_loss: 0.3470 - val_accuracy: 0.9859
Epoch 105/200
469/469 [==============================] - 2s 3ms/step - loss: 5.8015e-10 - accuracy: 1.0000 - val_loss: 0.3493 - val_accuracy: 0.9859
Epoch 106/200
469/469 [==============================] - 2s 3ms/step - loss: 4.7684e-10 - accuracy: 1.0000 - val_loss: 0.3514 - val_accuracy: 0.9859
Epoch 107/200
469/469 [==============================] - 2s 3ms/step - loss: 4.1922e-10 - accuracy: 1.0000 - val_loss: 0.3534 - val_accuracy: 0.9859
Epoch 108/200
469/469 [==============================] - 2s 4ms/step - loss: 3.5365e-10 - accuracy: 1.0000 - val_loss: 0.3553 - val_accuracy: 0.9859
Epoch 109/200
469/469 [==============================] - 2s 4ms/step - loss: 3.0001e-10 - accuracy: 1.0000 - val_loss: 0.3571 - val_accuracy: 0.9859
Epoch 110/200
469/469 [==============================] - 2s 4ms/step - loss: 2.5233e-10 - accuracy: 1.0000 - val_loss: 0.3588 - val_accuracy: 0.9859
Epoch 111/200
469/469 [==============================] - 2s 3ms/step - loss: 2.1656e-10 - accuracy: 1.0000 - val_loss: 0.3605 - val_accuracy: 0.9859
Epoch 112/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7285e-10 - accuracy: 1.0000 - val_loss: 0.3620 - val_accuracy: 0.9859
Epoch 113/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5299e-10 - accuracy: 1.0000 - val_loss: 0.3635 - val_accuracy: 0.9859
Epoch 114/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3312e-10 - accuracy: 1.0000 - val_loss: 0.3649 - val_accuracy: 0.9859
Epoch 115/200
469/469 [==============================] - 2s 4ms/step - loss: 1.0928e-10 - accuracy: 1.0000 - val_loss: 0.3663 - val_accuracy: 0.9859
Epoch 116/200
469/469 [==============================] - 2s 4ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.3676 - val_accuracy: 0.9859
Epoch 117/200
469/469 [==============================] - 2s 4ms/step - loss: 8.3446e-11 - accuracy: 1.0000 - val_loss: 0.3689 - val_accuracy: 0.9859
Epoch 118/200
469/469 [==============================] - 2s 4ms/step - loss: 6.5565e-11 - accuracy: 1.0000 - val_loss: 0.3701 - val_accuracy: 0.9859
Epoch 119/200
469/469 [==============================] - 2s 3ms/step - loss: 4.7684e-11 - accuracy: 1.0000 - val_loss: 0.3713 - val_accuracy: 0.9859
Epoch 120/200
469/469 [==============================] - 2s 3ms/step - loss: 4.1723e-11 - accuracy: 1.0000 - val_loss: 0.3724 - val_accuracy: 0.9859
Epoch 121/200
469/469 [==============================] - 2s 3ms/step - loss: 3.1789e-11 - accuracy: 1.0000 - val_loss: 0.3735 - val_accuracy: 0.9859
Epoch 122/200
469/469 [==============================] - 2s 3ms/step - loss: 2.9802e-11 - accuracy: 1.0000 - val_loss: 0.3746 - val_accuracy: 0.9859
Epoch 123/200
469/469 [==============================] - 2s 4ms/step - loss: 2.1855e-11 - accuracy: 1.0000 - val_loss: 0.3756 - val_accuracy: 0.9859
Epoch 124/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5895e-11 - accuracy: 1.0000 - val_loss: 0.3765 - val_accuracy: 0.9859
Epoch 125/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1921e-11 - accuracy: 1.0000 - val_loss: 0.3775 - val_accuracy: 0.9858
Epoch 126/200
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3783 - val_accuracy: 0.9858
Epoch 127/200
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3792 - val_accuracy: 0.9859
Epoch 128/200
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3800 - val_accuracy: 0.9859
Epoch 129/200
469/469 [==============================] - 2s 4ms/step - loss: 7.9473e-12 - accuracy: 1.0000 - val_loss: 0.3807 - val_accuracy: 0.9859
Epoch 130/200
469/469 [==============================] - 2s 4ms/step - loss: 5.9605e-12 - accuracy: 1.0000 - val_loss: 0.3814 - val_accuracy: 0.9859
Epoch 131/200
469/469 [==============================] - 2s 4ms/step - loss: 5.9605e-12 - accuracy: 1.0000 - val_loss: 0.3821 - val_accuracy: 0.9859
Epoch 132/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3827 - val_accuracy: 0.9858
Epoch 133/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3834 - val_accuracy: 0.9858
Epoch 134/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3839 - val_accuracy: 0.9858
Epoch 135/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3845 - val_accuracy: 0.9857
Epoch 136/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3850 - val_accuracy: 0.9857
Epoch 137/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3855 - val_accuracy: 0.9857
Epoch 138/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3860 - val_accuracy: 0.9857
Epoch 139/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3864 - val_accuracy: 0.9857
Epoch 140/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3869 - val_accuracy: 0.9857
Epoch 141/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3873 - val_accuracy: 0.9857
Epoch 142/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3877 - val_accuracy: 0.9857
Epoch 143/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3881 - val_accuracy: 0.9857
Epoch 144/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3885 - val_accuracy: 0.9857
Epoch 145/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3889 - val_accuracy: 0.9857
Epoch 146/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3892 - val_accuracy: 0.9857
Epoch 147/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3896 - val_accuracy: 0.9857
Epoch 148/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3899 - val_accuracy: 0.9857
Epoch 149/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3902 - val_accuracy: 0.9857
Epoch 150/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3905 - val_accuracy: 0.9857
Epoch 151/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3908 - val_accuracy: 0.9857
Epoch 152/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3911 - val_accuracy: 0.9857
Epoch 153/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3914 - val_accuracy: 0.9857
Epoch 154/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3917 - val_accuracy: 0.9857
Epoch 155/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3920 - val_accuracy: 0.9857
Epoch 156/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3922 - val_accuracy: 0.9857
Epoch 157/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3925 - val_accuracy: 0.9857
Epoch 158/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3927 - val_accuracy: 0.9857
Epoch 159/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3930 - val_accuracy: 0.9857
Epoch 160/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3932 - val_accuracy: 0.9857
Epoch 161/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3935 - val_accuracy: 0.9857
Epoch 162/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3937 - val_accuracy: 0.9858
Epoch 163/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3939 - val_accuracy: 0.9858
Epoch 164/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3942 - val_accuracy: 0.9858
Epoch 165/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3944 - val_accuracy: 0.9858
Epoch 166/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3946 - val_accuracy: 0.9858
Epoch 167/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3948 - val_accuracy: 0.9858
Epoch 168/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3950 - val_accuracy: 0.9858
Epoch 169/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3952 - val_accuracy: 0.9858
Epoch 170/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3954 - val_accuracy: 0.9858
Epoch 171/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3956 - val_accuracy: 0.9858
Epoch 172/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3958 - val_accuracy: 0.9858
Epoch 173/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3960 - val_accuracy: 0.9858
Epoch 174/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3962 - val_accuracy: 0.9858
Epoch 175/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3963 - val_accuracy: 0.9858
Epoch 176/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3965 - val_accuracy: 0.9858
Epoch 177/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3967 - val_accuracy: 0.9858
Epoch 178/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3969 - val_accuracy: 0.9858
Epoch 179/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3970 - val_accuracy: 0.9858
Epoch 180/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3972 - val_accuracy: 0.9858
Epoch 181/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3974 - val_accuracy: 0.9858
Epoch 182/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3975 - val_accuracy: 0.9858
Epoch 183/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3977 - val_accuracy: 0.9858
Epoch 184/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3979 - val_accuracy: 0.9858
Epoch 185/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3980 - val_accuracy: 0.9858
Epoch 186/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3982 - val_accuracy: 0.9858
Epoch 187/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3983 - val_accuracy: 0.9858
Epoch 188/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3985 - val_accuracy: 0.9858
Epoch 189/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3986 - val_accuracy: 0.9858
Epoch 190/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3988 - val_accuracy: 0.9858
Epoch 191/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3989 - val_accuracy: 0.9858
Epoch 192/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3990 - val_accuracy: 0.9858
Epoch 193/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3992 - val_accuracy: 0.9858
Epoch 194/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3993 - val_accuracy: 0.9858
Epoch 195/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3995 - val_accuracy: 0.9858
Epoch 196/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3996 - val_accuracy: 0.9858
Epoch 197/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3997 - val_accuracy: 0.9858
Epoch 198/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.9858
Epoch 199/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4000 - val_accuracy: 0.9858
Epoch 200/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4001 - val_accuracy: 0.9858

(d) Activation function: ReLU; initialization: Xavier initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        #tf.keras.layers.Dense(512,kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_6 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_36 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_37 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_38 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_39 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_40 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_41 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam() #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
historya3 = model.fit(X_train, y_train, batch_size=128, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
469/469 [==============================] - 2s 4ms/step - loss: 0.2291 - accuracy: 0.9307 - val_loss: 0.1238 - val_accuracy: 0.9626
Epoch 2/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0967 - accuracy: 0.9708 - val_loss: 0.1124 - val_accuracy: 0.9680
Epoch 3/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0650 - accuracy: 0.9804 - val_loss: 0.0921 - val_accuracy: 0.9751
Epoch 4/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0542 - accuracy: 0.9841 - val_loss: 0.0896 - val_accuracy: 0.9764
Epoch 5/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0432 - accuracy: 0.9869 - val_loss: 0.0926 - val_accuracy: 0.9761
Epoch 6/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0367 - accuracy: 0.9890 - val_loss: 0.0720 - val_accuracy: 0.9811
Epoch 7/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0320 - accuracy: 0.9906 - val_loss: 0.0765 - val_accuracy: 0.9798
Epoch 8/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0304 - accuracy: 0.9911 - val_loss: 0.0933 - val_accuracy: 0.9769
Epoch 9/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0254 - accuracy: 0.9925 - val_loss: 0.0913 - val_accuracy: 0.9799
Epoch 10/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0246 - accuracy: 0.9929 - val_loss: 0.1032 - val_accuracy: 0.9768
Epoch 11/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0222 - accuracy: 0.9935 - val_loss: 0.1019 - val_accuracy: 0.9807
Epoch 12/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0200 - accuracy: 0.9945 - val_loss: 0.1137 - val_accuracy: 0.9773
Epoch 13/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0205 - accuracy: 0.9942 - val_loss: 0.0887 - val_accuracy: 0.9817
Epoch 14/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0162 - accuracy: 0.9952 - val_loss: 0.1121 - val_accuracy: 0.9807
Epoch 15/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 0.0970 - val_accuracy: 0.9818
Epoch 16/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0162 - accuracy: 0.9960 - val_loss: 0.1034 - val_accuracy: 0.9803
Epoch 17/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9960 - val_loss: 0.0879 - val_accuracy: 0.9826
Epoch 18/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0168 - accuracy: 0.9953 - val_loss: 0.0903 - val_accuracy: 0.9841
Epoch 19/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9963 - val_loss: 0.0978 - val_accuracy: 0.9828
Epoch 20/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0140 - accuracy: 0.9961 - val_loss: 0.0921 - val_accuracy: 0.9826
Epoch 21/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9962 - val_loss: 0.1006 - val_accuracy: 0.9816
Epoch 22/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0099 - accuracy: 0.9974 - val_loss: 0.1134 - val_accuracy: 0.9813
Epoch 23/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0147 - accuracy: 0.9959 - val_loss: 0.0989 - val_accuracy: 0.9838
Epoch 24/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0125 - accuracy: 0.9971 - val_loss: 0.1140 - val_accuracy: 0.9834
Epoch 25/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0080 - accuracy: 0.9979 - val_loss: 0.1232 - val_accuracy: 0.9793
Epoch 26/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0102 - accuracy: 0.9975 - val_loss: 0.1224 - val_accuracy: 0.9801
Epoch 27/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9967 - val_loss: 0.1040 - val_accuracy: 0.9800
Epoch 28/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9967 - val_loss: 0.1114 - val_accuracy: 0.9818
Epoch 29/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0090 - accuracy: 0.9980 - val_loss: 0.0949 - val_accuracy: 0.9854
Epoch 30/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0104 - accuracy: 0.9975 - val_loss: 0.1287 - val_accuracy: 0.9817
Epoch 31/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0109 - accuracy: 0.9975 - val_loss: 0.1123 - val_accuracy: 0.9809
Epoch 32/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9972 - val_loss: 0.1041 - val_accuracy: 0.9835
Epoch 33/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0091 - accuracy: 0.9981 - val_loss: 0.1234 - val_accuracy: 0.9838
Epoch 34/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0066 - accuracy: 0.9983 - val_loss: 0.1298 - val_accuracy: 0.9836
Epoch 35/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0073 - accuracy: 0.9983 - val_loss: 0.1012 - val_accuracy: 0.9837
Epoch 36/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0066 - accuracy: 0.9984 - val_loss: 0.1138 - val_accuracy: 0.9824
Epoch 37/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0056 - accuracy: 0.9987 - val_loss: 0.1367 - val_accuracy: 0.9824
Epoch 38/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9970 - val_loss: 0.1444 - val_accuracy: 0.9815
Epoch 39/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0080 - accuracy: 0.9981 - val_loss: 0.1229 - val_accuracy: 0.9834
Epoch 40/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0091 - accuracy: 0.9981 - val_loss: 0.1500 - val_accuracy: 0.9838
Epoch 41/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0079 - accuracy: 0.9984 - val_loss: 0.1131 - val_accuracy: 0.9824
Epoch 42/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9977 - val_loss: 0.1241 - val_accuracy: 0.9814
Epoch 43/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.1374 - val_accuracy: 0.9824
Epoch 44/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0074 - accuracy: 0.9981 - val_loss: 0.1338 - val_accuracy: 0.9828
Epoch 45/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 0.1270 - val_accuracy: 0.9829
Epoch 46/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0073 - accuracy: 0.9984 - val_loss: 0.1266 - val_accuracy: 0.9812
Epoch 47/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9990 - val_loss: 0.1244 - val_accuracy: 0.9819
Epoch 48/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0071 - accuracy: 0.9984 - val_loss: 0.1085 - val_accuracy: 0.9834
Epoch 49/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0076 - accuracy: 0.9983 - val_loss: 0.1432 - val_accuracy: 0.9831
Epoch 50/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 0.1296 - val_accuracy: 0.9840
Epoch 51/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0078 - accuracy: 0.9982 - val_loss: 0.1483 - val_accuracy: 0.9838
Epoch 52/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0061 - accuracy: 0.9986 - val_loss: 0.1252 - val_accuracy: 0.9840
Epoch 53/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0085 - accuracy: 0.9984 - val_loss: 0.1434 - val_accuracy: 0.9849
Epoch 54/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.1637 - val_accuracy: 0.9851
Epoch 55/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0060 - accuracy: 0.9988 - val_loss: 0.1263 - val_accuracy: 0.9838
Epoch 56/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.1191 - val_accuracy: 0.9855
Epoch 57/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0060 - accuracy: 0.9988 - val_loss: 0.1647 - val_accuracy: 0.9816
Epoch 58/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0089 - accuracy: 0.9986 - val_loss: 0.1162 - val_accuracy: 0.9838
Epoch 59/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0062 - accuracy: 0.9986 - val_loss: 0.1210 - val_accuracy: 0.9825
Epoch 60/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 0.1374 - val_accuracy: 0.9834
Epoch 61/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.1455 - val_accuracy: 0.9828
Epoch 62/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0103 - accuracy: 0.9984 - val_loss: 0.1475 - val_accuracy: 0.9813
Epoch 63/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0074 - accuracy: 0.9986 - val_loss: 0.1164 - val_accuracy: 0.9836
Epoch 64/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0037 - accuracy: 0.9992 - val_loss: 0.1318 - val_accuracy: 0.9830
Epoch 65/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0107 - accuracy: 0.9980 - val_loss: 0.1453 - val_accuracy: 0.9823
Epoch 66/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0049 - accuracy: 0.9990 - val_loss: 0.1541 - val_accuracy: 0.9838
Epoch 67/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1382 - val_accuracy: 0.9832
Epoch 68/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0050 - accuracy: 0.9991 - val_loss: 0.1491 - val_accuracy: 0.9837
Epoch 69/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.1863 - val_accuracy: 0.9836
Epoch 70/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0061 - accuracy: 0.9985 - val_loss: 0.2057 - val_accuracy: 0.9807
Epoch 71/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1716 - val_accuracy: 0.9838
Epoch 72/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0079 - accuracy: 0.9987 - val_loss: 0.1275 - val_accuracy: 0.9843
Epoch 73/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9983 - val_loss: 0.1441 - val_accuracy: 0.9830
Epoch 74/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.1443 - val_accuracy: 0.9842
Epoch 75/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0065 - accuracy: 0.9988 - val_loss: 0.1519 - val_accuracy: 0.9840
Epoch 76/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.1472 - val_accuracy: 0.9831
Epoch 77/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0072 - accuracy: 0.9987 - val_loss: 0.1461 - val_accuracy: 0.9837
Epoch 78/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 0.1609 - val_accuracy: 0.9829
Epoch 79/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1836 - val_accuracy: 0.9846
Epoch 80/200
469/469 [==============================] - 2s 4ms/step - loss: 3.0217e-05 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9852
Epoch 81/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9975 - val_loss: 0.1494 - val_accuracy: 0.9829
Epoch 82/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 0.1611 - val_accuracy: 0.9834
Epoch 83/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1456 - val_accuracy: 0.9852
Epoch 84/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.1556 - val_accuracy: 0.9848
Epoch 85/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0110 - accuracy: 0.9978 - val_loss: 0.1469 - val_accuracy: 0.9822
Epoch 86/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.1325 - val_accuracy: 0.9852
Epoch 87/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.1256 - val_accuracy: 0.9843
Epoch 88/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.1624 - val_accuracy: 0.9850
Epoch 89/200
469/469 [==============================] - 2s 4ms/step - loss: 6.8777e-04 - accuracy: 0.9999 - val_loss: 0.1632 - val_accuracy: 0.9852
Epoch 90/200
469/469 [==============================] - 2s 3ms/step - loss: 6.1643e-04 - accuracy: 0.9999 - val_loss: 0.1674 - val_accuracy: 0.9860
Epoch 91/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7304e-05 - accuracy: 1.0000 - val_loss: 0.1812 - val_accuracy: 0.9859
Epoch 92/200
469/469 [==============================] - 2s 4ms/step - loss: 2.7191e-06 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9863
Epoch 93/200
469/469 [==============================] - 2s 3ms/step - loss: 3.4179e-07 - accuracy: 1.0000 - val_loss: 0.2250 - val_accuracy: 0.9861
Epoch 94/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2425e-07 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9860
Epoch 95/200
469/469 [==============================] - 2s 4ms/step - loss: 5.8452e-08 - accuracy: 1.0000 - val_loss: 0.2571 - val_accuracy: 0.9861
Epoch 96/200
469/469 [==============================] - 2s 3ms/step - loss: 3.2900e-08 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9861
Epoch 97/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0553e-08 - accuracy: 1.0000 - val_loss: 0.2759 - val_accuracy: 0.9861
Epoch 98/200
469/469 [==============================] - 2s 3ms/step - loss: 1.3876e-08 - accuracy: 1.0000 - val_loss: 0.2835 - val_accuracy: 0.9860
Epoch 99/200
469/469 [==============================] - 2s 3ms/step - loss: 9.6817e-09 - accuracy: 1.0000 - val_loss: 0.2898 - val_accuracy: 0.9861
Epoch 100/200
469/469 [==============================] - 2s 3ms/step - loss: 7.1068e-09 - accuracy: 1.0000 - val_loss: 0.2958 - val_accuracy: 0.9862
Epoch 101/200
469/469 [==============================] - 2s 4ms/step - loss: 5.3207e-09 - accuracy: 1.0000 - val_loss: 0.3010 - val_accuracy: 0.9861
Epoch 102/200
469/469 [==============================] - 2s 3ms/step - loss: 4.0849e-09 - accuracy: 1.0000 - val_loss: 0.3060 - val_accuracy: 0.9861
Epoch 103/200
469/469 [==============================] - 2s 4ms/step - loss: 3.1928e-09 - accuracy: 1.0000 - val_loss: 0.3106 - val_accuracy: 0.9860
Epoch 104/200
469/469 [==============================] - 2s 4ms/step - loss: 2.4815e-09 - accuracy: 1.0000 - val_loss: 0.3150 - val_accuracy: 0.9861
Epoch 105/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9590e-09 - accuracy: 1.0000 - val_loss: 0.3192 - val_accuracy: 0.9862
Epoch 106/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5597e-09 - accuracy: 1.0000 - val_loss: 0.3228 - val_accuracy: 0.9862
Epoch 107/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2577e-09 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9862
Epoch 108/200
469/469 [==============================] - 2s 4ms/step - loss: 1.0093e-09 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9862
Epoch 109/200
469/469 [==============================] - 2s 3ms/step - loss: 8.3248e-10 - accuracy: 1.0000 - val_loss: 0.3334 - val_accuracy: 0.9862
Epoch 110/200
469/469 [==============================] - 2s 4ms/step - loss: 6.9340e-10 - accuracy: 1.0000 - val_loss: 0.3365 - val_accuracy: 0.9861
Epoch 111/200
469/469 [==============================] - 2s 4ms/step - loss: 5.8015e-10 - accuracy: 1.0000 - val_loss: 0.3393 - val_accuracy: 0.9861
Epoch 112/200
469/469 [==============================] - 2s 4ms/step - loss: 4.7882e-10 - accuracy: 1.0000 - val_loss: 0.3423 - val_accuracy: 0.9861
Epoch 113/200
469/469 [==============================] - 2s 3ms/step - loss: 3.9736e-10 - accuracy: 1.0000 - val_loss: 0.3450 - val_accuracy: 0.9861
Epoch 114/200
469/469 [==============================] - 2s 4ms/step - loss: 3.3776e-10 - accuracy: 1.0000 - val_loss: 0.3476 - val_accuracy: 0.9861
Epoch 115/200
469/469 [==============================] - 2s 4ms/step - loss: 2.7418e-10 - accuracy: 1.0000 - val_loss: 0.3502 - val_accuracy: 0.9860
Epoch 116/200
469/469 [==============================] - 2s 3ms/step - loss: 2.4041e-10 - accuracy: 1.0000 - val_loss: 0.3528 - val_accuracy: 0.9859
Epoch 117/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0266e-10 - accuracy: 1.0000 - val_loss: 0.3552 - val_accuracy: 0.9860
Epoch 118/200
469/469 [==============================] - 2s 4ms/step - loss: 1.7087e-10 - accuracy: 1.0000 - val_loss: 0.3574 - val_accuracy: 0.9860
Epoch 119/200
469/469 [==============================] - 2s 4ms/step - loss: 1.4901e-10 - accuracy: 1.0000 - val_loss: 0.3596 - val_accuracy: 0.9860
Epoch 120/200
469/469 [==============================] - 2s 4ms/step - loss: 1.2318e-10 - accuracy: 1.0000 - val_loss: 0.3619 - val_accuracy: 0.9860
Epoch 121/200
469/469 [==============================] - 2s 3ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.3642 - val_accuracy: 0.9860
Epoch 122/200
469/469 [==============================] - 2s 4ms/step - loss: 7.9473e-11 - accuracy: 1.0000 - val_loss: 0.3665 - val_accuracy: 0.9860
Epoch 123/200
469/469 [==============================] - 2s 3ms/step - loss: 6.5565e-11 - accuracy: 1.0000 - val_loss: 0.3687 - val_accuracy: 0.9860
Epoch 124/200
469/469 [==============================] - 2s 3ms/step - loss: 5.5631e-11 - accuracy: 1.0000 - val_loss: 0.3706 - val_accuracy: 0.9860
Epoch 125/200
469/469 [==============================] - 2s 4ms/step - loss: 4.7684e-11 - accuracy: 1.0000 - val_loss: 0.3725 - val_accuracy: 0.9860
Epoch 126/200
469/469 [==============================] - 2s 3ms/step - loss: 3.3776e-11 - accuracy: 1.0000 - val_loss: 0.3742 - val_accuracy: 0.9860
Epoch 127/200
469/469 [==============================] - 2s 4ms/step - loss: 2.5829e-11 - accuracy: 1.0000 - val_loss: 0.3761 - val_accuracy: 0.9859
Epoch 128/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-11 - accuracy: 1.0000 - val_loss: 0.3779 - val_accuracy: 0.9859
Epoch 129/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3908e-11 - accuracy: 1.0000 - val_loss: 0.3796 - val_accuracy: 0.9859
Epoch 130/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3908e-11 - accuracy: 1.0000 - val_loss: 0.3811 - val_accuracy: 0.9859
Epoch 131/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1921e-11 - accuracy: 1.0000 - val_loss: 0.3826 - val_accuracy: 0.9859
Epoch 132/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1921e-11 - accuracy: 1.0000 - val_loss: 0.3839 - val_accuracy: 0.9859
Epoch 133/200
469/469 [==============================] - 2s 3ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3853 - val_accuracy: 0.9859
Epoch 134/200
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3865 - val_accuracy: 0.9858
Epoch 135/200
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3876 - val_accuracy: 0.9858
Epoch 136/200
469/469 [==============================] - 2s 3ms/step - loss: 9.9341e-12 - accuracy: 1.0000 - val_loss: 0.3887 - val_accuracy: 0.9858
Epoch 137/200
469/469 [==============================] - 2s 3ms/step - loss: 7.9473e-12 - accuracy: 1.0000 - val_loss: 0.3896 - val_accuracy: 0.9857
Epoch 138/200
469/469 [==============================] - 2s 3ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.3908 - val_accuracy: 0.9857
Epoch 139/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3918 - val_accuracy: 0.9857
Epoch 140/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3928 - val_accuracy: 0.9857
Epoch 141/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3938 - val_accuracy: 0.9857
Epoch 142/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3946 - val_accuracy: 0.9857
Epoch 143/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3955 - val_accuracy: 0.9857
Epoch 144/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3963 - val_accuracy: 0.9857
Epoch 145/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3970 - val_accuracy: 0.9857
Epoch 146/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3978 - val_accuracy: 0.9857
Epoch 147/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3985 - val_accuracy: 0.9857
Epoch 148/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3992 - val_accuracy: 0.9857
Epoch 149/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3998 - val_accuracy: 0.9857
Epoch 150/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4005 - val_accuracy: 0.9857
Epoch 151/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4011 - val_accuracy: 0.9857
Epoch 152/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4016 - val_accuracy: 0.9857
Epoch 153/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4022 - val_accuracy: 0.9857
Epoch 154/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4028 - val_accuracy: 0.9857
Epoch 155/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4033 - val_accuracy: 0.9857
Epoch 156/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4038 - val_accuracy: 0.9857
Epoch 157/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4043 - val_accuracy: 0.9857
Epoch 158/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4047 - val_accuracy: 0.9857
Epoch 159/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4052 - val_accuracy: 0.9857
Epoch 160/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4057 - val_accuracy: 0.9857
Epoch 161/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4061 - val_accuracy: 0.9857
Epoch 162/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4065 - val_accuracy: 0.9856
Epoch 163/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4069 - val_accuracy: 0.9856
Epoch 164/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4073 - val_accuracy: 0.9856
Epoch 165/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4077 - val_accuracy: 0.9856
Epoch 166/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4081 - val_accuracy: 0.9856
Epoch 167/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4085 - val_accuracy: 0.9856
Epoch 168/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4088 - val_accuracy: 0.9856
Epoch 169/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4092 - val_accuracy: 0.9856
Epoch 170/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4096 - val_accuracy: 0.9856
Epoch 171/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4099 - val_accuracy: 0.9856
Epoch 172/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4102 - val_accuracy: 0.9856
Epoch 173/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4106 - val_accuracy: 0.9856
Epoch 174/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4109 - val_accuracy: 0.9856
Epoch 175/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4112 - val_accuracy: 0.9856
Epoch 176/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4115 - val_accuracy: 0.9856
Epoch 177/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4118 - val_accuracy: 0.9856
Epoch 178/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4121 - val_accuracy: 0.9856
Epoch 179/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4124 - val_accuracy: 0.9856
Epoch 180/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4127 - val_accuracy: 0.9856
Epoch 181/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4130 - val_accuracy: 0.9856
Epoch 182/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4133 - val_accuracy: 0.9856
Epoch 183/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4136 - val_accuracy: 0.9856
Epoch 184/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4138 - val_accuracy: 0.9856
Epoch 185/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4141 - val_accuracy: 0.9856
Epoch 186/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4144 - val_accuracy: 0.9856
Epoch 187/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4146 - val_accuracy: 0.9856
Epoch 188/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4149 - val_accuracy: 0.9856
Epoch 189/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4151 - val_accuracy: 0.9856
Epoch 190/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4154 - val_accuracy: 0.9856
Epoch 191/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4156 - val_accuracy: 0.9856
Epoch 192/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4159 - val_accuracy: 0.9856
Epoch 193/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4161 - val_accuracy: 0.9856
Epoch 194/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4163 - val_accuracy: 0.9856
Epoch 195/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4166 - val_accuracy: 0.9856
Epoch 196/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4168 - val_accuracy: 0.9856
Epoch 197/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4170 - val_accuracy: 0.9856
Epoch 198/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4172 - val_accuracy: 0.9856
Epoch 199/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4174 - val_accuracy: 0.9856
Epoch 200/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.4176 - val_accuracy: 0.9856

(e) Activation function: ReLU; initialization: Kaiming He’s initializer

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.HeNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        #tf.keras.layers.Dense(512,kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(512,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_7 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_42 (Dense)             (None, 512)               401920    
_________________________________________________________________
dense_43 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_44 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_45 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_46 (Dense)             (None, 512)               262656    
_________________________________________________________________
dense_47 (Dense)             (None, 10)                5130      
=================================================================
Total params: 1,457,674
Trainable params: 1,457,674
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam() #learning_rate=1.0 for SGD
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) #categorical_crossentropy
historya4 = model.fit(X_train, y_train, batch_size=128, epochs=200, validation_data=(X_test, y_test))
Epoch 1/200
469/469 [==============================] - 2s 4ms/step - loss: 0.2093 - accuracy: 0.9358 - val_loss: 0.1174 - val_accuracy: 0.9653
Epoch 2/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0905 - accuracy: 0.9730 - val_loss: 0.0985 - val_accuracy: 0.9698
Epoch 3/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0646 - accuracy: 0.9798 - val_loss: 0.1142 - val_accuracy: 0.9695
Epoch 4/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0512 - accuracy: 0.9847 - val_loss: 0.0903 - val_accuracy: 0.9774
Epoch 5/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0438 - accuracy: 0.9865 - val_loss: 0.0935 - val_accuracy: 0.9761
Epoch 6/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0321 - accuracy: 0.9900 - val_loss: 0.1073 - val_accuracy: 0.9723
Epoch 7/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0302 - accuracy: 0.9909 - val_loss: 0.0922 - val_accuracy: 0.9771
Epoch 8/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0301 - accuracy: 0.9909 - val_loss: 0.0836 - val_accuracy: 0.9775
Epoch 9/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0254 - accuracy: 0.9925 - val_loss: 0.0914 - val_accuracy: 0.9759
Epoch 10/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0227 - accuracy: 0.9933 - val_loss: 0.0866 - val_accuracy: 0.9782
Epoch 11/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0246 - accuracy: 0.9928 - val_loss: 0.0697 - val_accuracy: 0.9821
Epoch 12/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0202 - accuracy: 0.9941 - val_loss: 0.0953 - val_accuracy: 0.9787
Epoch 13/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0194 - accuracy: 0.9944 - val_loss: 0.1082 - val_accuracy: 0.9764
Epoch 14/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0187 - accuracy: 0.9947 - val_loss: 0.1104 - val_accuracy: 0.9800
Epoch 15/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9955 - val_loss: 0.0843 - val_accuracy: 0.9824
Epoch 16/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9951 - val_loss: 0.0967 - val_accuracy: 0.9818
Epoch 17/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.1141 - val_accuracy: 0.9781
Epoch 18/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.1209 - val_accuracy: 0.9769
Epoch 19/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0177 - accuracy: 0.9954 - val_loss: 0.1097 - val_accuracy: 0.9798
Epoch 20/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0117 - accuracy: 0.9969 - val_loss: 0.1034 - val_accuracy: 0.9805
Epoch 21/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9969 - val_loss: 0.0970 - val_accuracy: 0.9815
Epoch 22/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0103 - accuracy: 0.9973 - val_loss: 0.0958 - val_accuracy: 0.9809
Epoch 23/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.0971 - val_accuracy: 0.9800
Epoch 24/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0124 - accuracy: 0.9969 - val_loss: 0.1026 - val_accuracy: 0.9823
Epoch 25/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9968 - val_loss: 0.1028 - val_accuracy: 0.9840
Epoch 26/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0135 - accuracy: 0.9969 - val_loss: 0.1196 - val_accuracy: 0.9822
Epoch 27/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0113 - accuracy: 0.9971 - val_loss: 0.1103 - val_accuracy: 0.9827
Epoch 28/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0099 - accuracy: 0.9975 - val_loss: 0.1227 - val_accuracy: 0.9787
Epoch 29/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0107 - accuracy: 0.9974 - val_loss: 0.1318 - val_accuracy: 0.9819
Epoch 30/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9966 - val_loss: 0.1321 - val_accuracy: 0.9794
Epoch 31/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0110 - accuracy: 0.9975 - val_loss: 0.1303 - val_accuracy: 0.9803
Epoch 32/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0077 - accuracy: 0.9980 - val_loss: 0.1664 - val_accuracy: 0.9808
Epoch 33/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0104 - accuracy: 0.9977 - val_loss: 0.1329 - val_accuracy: 0.9827
Epoch 34/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9987 - val_loss: 0.1405 - val_accuracy: 0.9810
Epoch 35/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9972 - val_loss: 0.1255 - val_accuracy: 0.9849
Epoch 36/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9970 - val_loss: 0.1166 - val_accuracy: 0.9793
Epoch 37/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0106 - accuracy: 0.9972 - val_loss: 0.1248 - val_accuracy: 0.9836
Epoch 38/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0078 - accuracy: 0.9980 - val_loss: 0.1358 - val_accuracy: 0.9820
Epoch 39/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0098 - accuracy: 0.9979 - val_loss: 0.1356 - val_accuracy: 0.9815
Epoch 40/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0073 - accuracy: 0.9986 - val_loss: 0.1402 - val_accuracy: 0.9822
Epoch 41/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0095 - accuracy: 0.9979 - val_loss: 0.1297 - val_accuracy: 0.9843
Epoch 42/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.1479 - val_accuracy: 0.9816
Epoch 43/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9972 - val_loss: 0.1274 - val_accuracy: 0.9841
Epoch 44/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0066 - accuracy: 0.9984 - val_loss: 0.1373 - val_accuracy: 0.9840
Epoch 45/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0061 - accuracy: 0.9985 - val_loss: 0.1484 - val_accuracy: 0.9842
Epoch 46/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 0.1566 - val_accuracy: 0.9821
Epoch 47/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0089 - accuracy: 0.9981 - val_loss: 0.1253 - val_accuracy: 0.9832
Epoch 48/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0091 - accuracy: 0.9982 - val_loss: 0.1427 - val_accuracy: 0.9831
Epoch 49/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0067 - accuracy: 0.9986 - val_loss: 0.1455 - val_accuracy: 0.9839
Epoch 50/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0057 - accuracy: 0.9989 - val_loss: 0.1449 - val_accuracy: 0.9827
Epoch 51/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0084 - accuracy: 0.9982 - val_loss: 0.1672 - val_accuracy: 0.9833
Epoch 52/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0091 - accuracy: 0.9983 - val_loss: 0.1455 - val_accuracy: 0.9818
Epoch 53/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9975 - val_loss: 0.1809 - val_accuracy: 0.9785
Epoch 54/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0101 - accuracy: 0.9979 - val_loss: 0.1631 - val_accuracy: 0.9819
Epoch 55/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0076 - accuracy: 0.9982 - val_loss: 0.1181 - val_accuracy: 0.9818
Epoch 56/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1516 - val_accuracy: 0.9829
Epoch 57/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 0.1584 - val_accuracy: 0.9814
Epoch 58/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.1644 - val_accuracy: 0.9821
Epoch 59/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0040 - accuracy: 0.9993 - val_loss: 0.1794 - val_accuracy: 0.9799
Epoch 60/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9969 - val_loss: 0.1640 - val_accuracy: 0.9840
Epoch 61/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.1561 - val_accuracy: 0.9844
Epoch 62/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.2156 - val_accuracy: 0.9805
Epoch 63/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9984 - val_loss: 0.1427 - val_accuracy: 0.9842
Epoch 64/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.1883 - val_accuracy: 0.9779
Epoch 65/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9991 - val_loss: 0.1504 - val_accuracy: 0.9825
Epoch 66/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0077 - accuracy: 0.9986 - val_loss: 0.1935 - val_accuracy: 0.9828
Epoch 67/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0068 - accuracy: 0.9985 - val_loss: 0.2054 - val_accuracy: 0.9800
Epoch 68/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9981 - val_loss: 0.2093 - val_accuracy: 0.9804
Epoch 69/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0072 - accuracy: 0.9987 - val_loss: 0.1978 - val_accuracy: 0.9821
Epoch 70/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 0.1696 - val_accuracy: 0.9820
Epoch 71/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1917 - val_accuracy: 0.9807
Epoch 72/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9992 - val_loss: 0.1642 - val_accuracy: 0.9851
Epoch 73/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0053 - accuracy: 0.9991 - val_loss: 0.1578 - val_accuracy: 0.9843
Epoch 74/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0084 - accuracy: 0.9982 - val_loss: 0.1628 - val_accuracy: 0.9836
Epoch 75/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9985 - val_loss: 0.1574 - val_accuracy: 0.9813
Epoch 76/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 0.2003 - val_accuracy: 0.9794
Epoch 77/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0067 - accuracy: 0.9989 - val_loss: 0.1624 - val_accuracy: 0.9841
Epoch 78/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0068 - accuracy: 0.9990 - val_loss: 0.1470 - val_accuracy: 0.9828
Epoch 79/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 0.1542 - val_accuracy: 0.9840
Epoch 80/200
469/469 [==============================] - 2s 3ms/step - loss: 7.3968e-04 - accuracy: 0.9998 - val_loss: 0.1885 - val_accuracy: 0.9841
Epoch 81/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0050 - accuracy: 0.9991 - val_loss: 0.1721 - val_accuracy: 0.9810
Epoch 82/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0159 - accuracy: 0.9977 - val_loss: 0.1768 - val_accuracy: 0.9804
Epoch 83/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0071 - accuracy: 0.9987 - val_loss: 0.1396 - val_accuracy: 0.9848
Epoch 84/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.1574 - val_accuracy: 0.9824
Epoch 85/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.2658 - val_accuracy: 0.9803
Epoch 86/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0045 - accuracy: 0.9993 - val_loss: 0.1905 - val_accuracy: 0.9825
Epoch 87/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0066 - accuracy: 0.9994 - val_loss: 0.1600 - val_accuracy: 0.9832
Epoch 88/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.2075 - val_accuracy: 0.9810
Epoch 89/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.1558 - val_accuracy: 0.9822
Epoch 90/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 0.1730 - val_accuracy: 0.9850
Epoch 91/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0101 - accuracy: 0.9985 - val_loss: 0.1607 - val_accuracy: 0.9847
Epoch 92/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0067 - accuracy: 0.9988 - val_loss: 0.2095 - val_accuracy: 0.9828
Epoch 93/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0030 - accuracy: 0.9995 - val_loss: 0.2073 - val_accuracy: 0.9841
Epoch 94/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.2250 - val_accuracy: 0.9836
Epoch 95/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0065 - accuracy: 0.9990 - val_loss: 0.1527 - val_accuracy: 0.9822
Epoch 96/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1973 - val_accuracy: 0.9811
Epoch 97/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.2609 - val_accuracy: 0.9826
Epoch 98/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9989 - val_loss: 0.2434 - val_accuracy: 0.9844
Epoch 99/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0056 - accuracy: 0.9990 - val_loss: 0.1665 - val_accuracy: 0.9847
Epoch 100/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.1821 - val_accuracy: 0.9832
Epoch 101/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.2074 - val_accuracy: 0.9841
Epoch 102/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9984 - val_loss: 0.2045 - val_accuracy: 0.9823
Epoch 103/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0083 - accuracy: 0.9988 - val_loss: 0.1885 - val_accuracy: 0.9842
Epoch 104/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0017 - accuracy: 0.9997 - val_loss: 0.1869 - val_accuracy: 0.9845
Epoch 105/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0017 - accuracy: 0.9996 - val_loss: 0.2240 - val_accuracy: 0.9832
Epoch 106/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.2144 - val_accuracy: 0.9832
Epoch 107/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9994 - val_loss: 0.1877 - val_accuracy: 0.9824
Epoch 108/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0130 - accuracy: 0.9978 - val_loss: 0.2717 - val_accuracy: 0.9799
Epoch 109/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0038 - accuracy: 0.9992 - val_loss: 0.2312 - val_accuracy: 0.9828
Epoch 110/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.2224 - val_accuracy: 0.9848
Epoch 111/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.3081 - val_accuracy: 0.9829
Epoch 112/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.2012 - val_accuracy: 0.9800
Epoch 113/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0045 - accuracy: 0.9990 - val_loss: 0.2208 - val_accuracy: 0.9840
Epoch 114/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0059 - accuracy: 0.9991 - val_loss: 0.2775 - val_accuracy: 0.9825
Epoch 115/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0038 - accuracy: 0.9991 - val_loss: 0.2096 - val_accuracy: 0.9838
Epoch 116/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0046 - accuracy: 0.9994 - val_loss: 0.2373 - val_accuracy: 0.9839
Epoch 117/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.2777 - val_accuracy: 0.9850
Epoch 118/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0089 - accuracy: 0.9984 - val_loss: 0.3015 - val_accuracy: 0.9821
Epoch 119/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0092 - accuracy: 0.9990 - val_loss: 0.2616 - val_accuracy: 0.9841
Epoch 120/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0056 - accuracy: 0.9990 - val_loss: 0.1801 - val_accuracy: 0.9828
Epoch 121/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0037 - accuracy: 0.9993 - val_loss: 0.3050 - val_accuracy: 0.9812
Epoch 122/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0056 - accuracy: 0.9991 - val_loss: 0.2886 - val_accuracy: 0.9835
Epoch 123/200
469/469 [==============================] - 2s 4ms/step - loss: 7.0380e-04 - accuracy: 0.9998 - val_loss: 0.2756 - val_accuracy: 0.9846
Epoch 124/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 0.2519 - val_accuracy: 0.9832
Epoch 125/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0072 - accuracy: 0.9992 - val_loss: 0.2450 - val_accuracy: 0.9818
Epoch 126/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0075 - accuracy: 0.9987 - val_loss: 0.2015 - val_accuracy: 0.9823
Epoch 127/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 0.2941 - val_accuracy: 0.9796
Epoch 128/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0049 - accuracy: 0.9991 - val_loss: 0.2360 - val_accuracy: 0.9843
Epoch 129/200
469/469 [==============================] - 2s 4ms/step - loss: 7.4236e-04 - accuracy: 0.9999 - val_loss: 0.2844 - val_accuracy: 0.9835
Epoch 130/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0057 - accuracy: 0.9991 - val_loss: 0.1867 - val_accuracy: 0.9806
Epoch 131/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.2785 - val_accuracy: 0.9831
Epoch 132/200
469/469 [==============================] - 2s 4ms/step - loss: 3.0945e-04 - accuracy: 0.9999 - val_loss: 0.3273 - val_accuracy: 0.9840
Epoch 133/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9983 - val_loss: 0.1764 - val_accuracy: 0.9827
Epoch 134/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0048 - accuracy: 0.9993 - val_loss: 0.2371 - val_accuracy: 0.9804
Epoch 135/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.1933 - val_accuracy: 0.9832
Epoch 136/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0880e-05 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9829
Epoch 137/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0640e-06 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9832
Epoch 138/200
469/469 [==============================] - 2s 3ms/step - loss: 7.6387e-07 - accuracy: 1.0000 - val_loss: 0.2776 - val_accuracy: 0.9833
Epoch 139/200
469/469 [==============================] - 2s 3ms/step - loss: 3.1319e-07 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9832
Epoch 140/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0044e-07 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9833
Epoch 141/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3919e-07 - accuracy: 1.0000 - val_loss: 0.3027 - val_accuracy: 0.9833
Epoch 142/200
469/469 [==============================] - 2s 3ms/step - loss: 9.6982e-08 - accuracy: 1.0000 - val_loss: 0.3100 - val_accuracy: 0.9832
Epoch 143/200
469/469 [==============================] - 2s 3ms/step - loss: 6.9867e-08 - accuracy: 1.0000 - val_loss: 0.3174 - val_accuracy: 0.9833
Epoch 144/200
469/469 [==============================] - 2s 3ms/step - loss: 5.0588e-08 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9833
Epoch 145/200
469/469 [==============================] - 2s 3ms/step - loss: 3.7603e-08 - accuracy: 1.0000 - val_loss: 0.3304 - val_accuracy: 0.9833
Epoch 146/200
469/469 [==============================] - 2s 3ms/step - loss: 2.7702e-08 - accuracy: 1.0000 - val_loss: 0.3371 - val_accuracy: 0.9832
Epoch 147/200
469/469 [==============================] - 2s 4ms/step - loss: 2.0692e-08 - accuracy: 1.0000 - val_loss: 0.3431 - val_accuracy: 0.9831
Epoch 148/200
469/469 [==============================] - 2s 4ms/step - loss: 1.5479e-08 - accuracy: 1.0000 - val_loss: 0.3498 - val_accuracy: 0.9831
Epoch 149/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1782e-08 - accuracy: 1.0000 - val_loss: 0.3561 - val_accuracy: 0.9833
Epoch 150/200
469/469 [==============================] - 2s 4ms/step - loss: 9.0956e-09 - accuracy: 1.0000 - val_loss: 0.3621 - val_accuracy: 0.9834
Epoch 151/200
469/469 [==============================] - 2s 3ms/step - loss: 7.1207e-09 - accuracy: 1.0000 - val_loss: 0.3677 - val_accuracy: 0.9834
Epoch 152/200
469/469 [==============================] - 2s 3ms/step - loss: 5.5333e-09 - accuracy: 1.0000 - val_loss: 0.3736 - val_accuracy: 0.9834
Epoch 153/200
469/469 [==============================] - 2s 3ms/step - loss: 4.2776e-09 - accuracy: 1.0000 - val_loss: 0.3791 - val_accuracy: 0.9835
Epoch 154/200
469/469 [==============================] - 2s 3ms/step - loss: 3.3975e-09 - accuracy: 1.0000 - val_loss: 0.3845 - val_accuracy: 0.9835
Epoch 155/200
469/469 [==============================] - 2s 4ms/step - loss: 2.6663e-09 - accuracy: 1.0000 - val_loss: 0.3900 - val_accuracy: 0.9836
Epoch 156/200
469/469 [==============================] - 2s 3ms/step - loss: 2.0941e-09 - accuracy: 1.0000 - val_loss: 0.3957 - val_accuracy: 0.9836
Epoch 157/200
469/469 [==============================] - 2s 3ms/step - loss: 1.6212e-09 - accuracy: 1.0000 - val_loss: 0.4014 - val_accuracy: 0.9836
Epoch 158/200
469/469 [==============================] - 2s 3ms/step - loss: 1.2358e-09 - accuracy: 1.0000 - val_loss: 0.4068 - val_accuracy: 0.9836
Epoch 159/200
469/469 [==============================] - 2s 3ms/step - loss: 9.8745e-10 - accuracy: 1.0000 - val_loss: 0.4117 - val_accuracy: 0.9836
Epoch 160/200
469/469 [==============================] - 2s 3ms/step - loss: 7.7685e-10 - accuracy: 1.0000 - val_loss: 0.4167 - val_accuracy: 0.9837
Epoch 161/200
469/469 [==============================] - 2s 4ms/step - loss: 6.0995e-10 - accuracy: 1.0000 - val_loss: 0.4219 - val_accuracy: 0.9837
Epoch 162/200
469/469 [==============================] - 2s 3ms/step - loss: 4.7684e-10 - accuracy: 1.0000 - val_loss: 0.4268 - val_accuracy: 0.9838
Epoch 163/200
469/469 [==============================] - 2s 3ms/step - loss: 3.7352e-10 - accuracy: 1.0000 - val_loss: 0.4318 - val_accuracy: 0.9839
Epoch 164/200
469/469 [==============================] - 2s 3ms/step - loss: 2.8610e-10 - accuracy: 1.0000 - val_loss: 0.4367 - val_accuracy: 0.9839
Epoch 165/200
469/469 [==============================] - 2s 3ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.4410 - val_accuracy: 0.9839
Epoch 166/200
469/469 [==============================] - 2s 3ms/step - loss: 1.8477e-10 - accuracy: 1.0000 - val_loss: 0.4455 - val_accuracy: 0.9839
Epoch 167/200
469/469 [==============================] - 2s 3ms/step - loss: 1.5497e-10 - accuracy: 1.0000 - val_loss: 0.4495 - val_accuracy: 0.9839
Epoch 168/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1921e-10 - accuracy: 1.0000 - val_loss: 0.4536 - val_accuracy: 0.9839
Epoch 169/200
469/469 [==============================] - 2s 3ms/step - loss: 8.9407e-11 - accuracy: 1.0000 - val_loss: 0.4581 - val_accuracy: 0.9839
Epoch 170/200
469/469 [==============================] - 2s 3ms/step - loss: 7.5499e-11 - accuracy: 1.0000 - val_loss: 0.4618 - val_accuracy: 0.9839
Epoch 171/200
469/469 [==============================] - 2s 3ms/step - loss: 6.1591e-11 - accuracy: 1.0000 - val_loss: 0.4654 - val_accuracy: 0.9839
Epoch 172/200
469/469 [==============================] - 2s 3ms/step - loss: 4.7684e-11 - accuracy: 1.0000 - val_loss: 0.4688 - val_accuracy: 0.9839
Epoch 173/200
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-11 - accuracy: 1.0000 - val_loss: 0.4724 - val_accuracy: 0.9839
Epoch 174/200
469/469 [==============================] - 2s 3ms/step - loss: 3.5763e-11 - accuracy: 1.0000 - val_loss: 0.4755 - val_accuracy: 0.9839
Epoch 175/200
469/469 [==============================] - 2s 3ms/step - loss: 2.1855e-11 - accuracy: 1.0000 - val_loss: 0.4791 - val_accuracy: 0.9839
Epoch 176/200
469/469 [==============================] - 2s 4ms/step - loss: 1.3908e-11 - accuracy: 1.0000 - val_loss: 0.4826 - val_accuracy: 0.9840
Epoch 177/200
469/469 [==============================] - 2s 4ms/step - loss: 1.1921e-11 - accuracy: 1.0000 - val_loss: 0.4857 - val_accuracy: 0.9841
Epoch 178/200
469/469 [==============================] - 2s 3ms/step - loss: 1.1921e-11 - accuracy: 1.0000 - val_loss: 0.4885 - val_accuracy: 0.9840
Epoch 179/200
469/469 [==============================] - 2s 4ms/step - loss: 7.9473e-12 - accuracy: 1.0000 - val_loss: 0.4917 - val_accuracy: 0.9841
Epoch 180/200
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.4948 - val_accuracy: 0.9841
Epoch 181/200
469/469 [==============================] - 2s 3ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.4977 - val_accuracy: 0.9842
Epoch 182/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5008 - val_accuracy: 0.9842
Epoch 183/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5036 - val_accuracy: 0.9842
Epoch 184/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5061 - val_accuracy: 0.9842
Epoch 185/200
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5085 - val_accuracy: 0.9843
Epoch 186/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5107 - val_accuracy: 0.9843
Epoch 187/200
469/469 [==============================] - 2s 3ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.5128 - val_accuracy: 0.9843
Epoch 188/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5152 - val_accuracy: 0.9843
Epoch 189/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5173 - val_accuracy: 0.9842
Epoch 190/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5195 - val_accuracy: 0.9842
Epoch 191/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5214 - val_accuracy: 0.9842
Epoch 192/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5234 - val_accuracy: 0.9841
Epoch 193/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5252 - val_accuracy: 0.9841
Epoch 194/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5269 - val_accuracy: 0.9841
Epoch 195/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5285 - val_accuracy: 0.9841
Epoch 196/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5301 - val_accuracy: 0.9841
Epoch 197/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5316 - val_accuracy: 0.9841
Epoch 198/200
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5330 - val_accuracy: 0.9841
Epoch 199/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5344 - val_accuracy: 0.9840
Epoch 200/200
469/469 [==============================] - 2s 3ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.5357 - val_accuracy: 0.9840

Visualize Adam Results

In [ ]:
#loss_train = history.history['accuracy']
test_acc = historya.history['val_accuracy'][0:18]
test_acc1 = historya1.history['val_accuracy'][0:18]
test_acc2 = historya2.history['val_accuracy'][0:18]
test_acc3 = historya3.history['val_accuracy'][0:18]
test_acc4 = historya4.history['val_accuracy'][0:18]

epochs = range(0,18)
plt.figure(figsize=(20,10))
plt.plot(epochs, test_acc, 'r', label='Logistic: Normal')
plt.plot(epochs, test_acc1, 'r', label='Logistic: Xavier', linewidth=3)
plt.plot(epochs, test_acc2, 'b', label='ReLU: Normal')
plt.plot(epochs, test_acc3, 'b', label='ReLU: Xavier', linewidth=3)
plt.plot(epochs, test_acc4, 'black', label='ReLU: He')
plt.title('Adam')
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy %')
plt.legend()
plt.show()
In [ ]:
#loss_train = history.history['accuracy']
test_acc = historya.history['val_accuracy'][19:]
test_acc1 = historya1.history['val_accuracy'][19:]
test_acc2 = historya2.history['val_accuracy'][19:]
test_acc3 = historya3.history['val_accuracy'][19:]
test_acc4 = historya4.history['val_accuracy'][19:]

epochs = range(19,200)
plt.figure(figsize=(20,10))
plt.plot(epochs, test_acc, 'r', label='Logistic: Normal')
plt.plot(epochs, test_acc1, 'r', label='Logistic: Xavier', linewidth=3)
plt.plot(epochs, test_acc2, 'b', label='ReLU: Normal')
plt.plot(epochs, test_acc3, 'b', label='ReLU: Xavier', linewidth=3)
plt.plot(epochs, test_acc4, 'black', label='ReLU: He')
plt.title('Adam')
plt.xlabel('Epoch')
plt.ylabel('Test Accuracy %')
plt.legend()
plt.show()

Problem 4

Activation function: the logistic sigmoid function; initialization: Xavier initializer; no dropout

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 784)               0         
_________________________________________________________________
dense (Dense)                (None, 1024)              803840    
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_3 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_4 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_5 (Dense)              (None, 10)                10250     
=================================================================
Total params: 5,012,490
Trainable params: 5,012,490
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam()
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
history = model.fit(X_train, y_train, batch_size=128, epochs=500, validation_data=(X_test, y_test))
Epoch 1/500
469/469 [==============================] - 2s 5ms/step - loss: 1.0706 - accuracy: 0.5963 - val_loss: 0.3144 - val_accuracy: 0.9059
Epoch 2/500
469/469 [==============================] - 2s 4ms/step - loss: 0.2430 - accuracy: 0.9272 - val_loss: 0.1820 - val_accuracy: 0.9432
Epoch 3/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1600 - accuracy: 0.9518 - val_loss: 0.1574 - val_accuracy: 0.9541
Epoch 4/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1205 - accuracy: 0.9640 - val_loss: 0.1376 - val_accuracy: 0.9572
Epoch 5/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0967 - accuracy: 0.9710 - val_loss: 0.1078 - val_accuracy: 0.9700
Epoch 6/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0774 - accuracy: 0.9765 - val_loss: 0.1397 - val_accuracy: 0.9598
Epoch 7/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0657 - accuracy: 0.9798 - val_loss: 0.1110 - val_accuracy: 0.9693
Epoch 8/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0546 - accuracy: 0.9833 - val_loss: 0.1066 - val_accuracy: 0.9712
Epoch 9/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0469 - accuracy: 0.9857 - val_loss: 0.0909 - val_accuracy: 0.9751
Epoch 10/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0385 - accuracy: 0.9879 - val_loss: 0.0913 - val_accuracy: 0.9732
Epoch 11/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0319 - accuracy: 0.9904 - val_loss: 0.1101 - val_accuracy: 0.9731
Epoch 12/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0303 - accuracy: 0.9905 - val_loss: 0.0909 - val_accuracy: 0.9783
Epoch 13/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0254 - accuracy: 0.9924 - val_loss: 0.1022 - val_accuracy: 0.9748
Epoch 14/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0252 - accuracy: 0.9923 - val_loss: 0.0973 - val_accuracy: 0.9769
Epoch 15/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0201 - accuracy: 0.9942 - val_loss: 0.0888 - val_accuracy: 0.9799
Epoch 16/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0167 - accuracy: 0.9952 - val_loss: 0.0831 - val_accuracy: 0.9812
Epoch 17/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0187 - accuracy: 0.9942 - val_loss: 0.0966 - val_accuracy: 0.9786
Epoch 18/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9954 - val_loss: 0.1140 - val_accuracy: 0.9759
Epoch 19/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9950 - val_loss: 0.1012 - val_accuracy: 0.9798
Epoch 20/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9955 - val_loss: 0.0938 - val_accuracy: 0.9813
Epoch 21/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9963 - val_loss: 0.0929 - val_accuracy: 0.9813
Epoch 22/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0103 - accuracy: 0.9971 - val_loss: 0.0854 - val_accuracy: 0.9824
Epoch 23/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0105 - accuracy: 0.9970 - val_loss: 0.0861 - val_accuracy: 0.9829
Epoch 24/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.1058 - val_accuracy: 0.9809
Epoch 25/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9966 - val_loss: 0.0934 - val_accuracy: 0.9832
Epoch 26/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0074 - accuracy: 0.9979 - val_loss: 0.1188 - val_accuracy: 0.9814
Epoch 27/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9972 - val_loss: 0.1010 - val_accuracy: 0.9815
Epoch 28/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.0933 - val_accuracy: 0.9830
Epoch 29/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0088 - accuracy: 0.9974 - val_loss: 0.1032 - val_accuracy: 0.9815
Epoch 30/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 0.1225 - val_accuracy: 0.9815
Epoch 31/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0084 - accuracy: 0.9977 - val_loss: 0.1092 - val_accuracy: 0.9820
Epoch 32/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0059 - accuracy: 0.9986 - val_loss: 0.1302 - val_accuracy: 0.9746
Epoch 33/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0069 - accuracy: 0.9981 - val_loss: 0.1088 - val_accuracy: 0.9834
Epoch 34/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 0.1233 - val_accuracy: 0.9802
Epoch 35/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0076 - accuracy: 0.9979 - val_loss: 0.0882 - val_accuracy: 0.9826
Epoch 36/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0053 - accuracy: 0.9984 - val_loss: 0.0975 - val_accuracy: 0.9831
Epoch 37/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 0.1068 - val_accuracy: 0.9820
Epoch 38/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0058 - accuracy: 0.9984 - val_loss: 0.1038 - val_accuracy: 0.9817
Epoch 39/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.1000 - val_accuracy: 0.9835
Epoch 40/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0050 - accuracy: 0.9987 - val_loss: 0.0965 - val_accuracy: 0.9850
Epoch 41/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 0.0951 - val_accuracy: 0.9822
Epoch 42/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.1026 - val_accuracy: 0.9850
Epoch 43/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1345 - val_accuracy: 0.9779
Epoch 44/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.1131 - val_accuracy: 0.9836
Epoch 45/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0069 - accuracy: 0.9982 - val_loss: 0.1085 - val_accuracy: 0.9832
Epoch 46/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.1179 - val_accuracy: 0.9829
Epoch 47/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.1326 - val_accuracy: 0.9783
Epoch 48/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 0.0946 - val_accuracy: 0.9849
Epoch 49/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1099 - val_accuracy: 0.9806
Epoch 50/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.1021 - val_accuracy: 0.9839
Epoch 51/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.1058 - val_accuracy: 0.9843
Epoch 52/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1125 - val_accuracy: 0.9815
Epoch 53/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1121 - val_accuracy: 0.9813
Epoch 54/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.1285 - val_accuracy: 0.9819
Epoch 55/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0051 - accuracy: 0.9984 - val_loss: 0.1205 - val_accuracy: 0.9814
Epoch 56/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0039 - accuracy: 0.9990 - val_loss: 0.1145 - val_accuracy: 0.9826
Epoch 57/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.1089 - val_accuracy: 0.9829
Epoch 58/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1310 - val_accuracy: 0.9818
Epoch 59/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1104 - val_accuracy: 0.9827
Epoch 60/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.1262 - val_accuracy: 0.9820
Epoch 61/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.1172 - val_accuracy: 0.9828
Epoch 62/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 0.1235 - val_accuracy: 0.9813
Epoch 63/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.1069 - val_accuracy: 0.9839
Epoch 64/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1160 - val_accuracy: 0.9822
Epoch 65/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1080 - val_accuracy: 0.9840
Epoch 66/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 0.1197 - val_accuracy: 0.9829
Epoch 67/500
469/469 [==============================] - 2s 4ms/step - loss: 7.0570e-04 - accuracy: 0.9998 - val_loss: 0.1256 - val_accuracy: 0.9845
Epoch 68/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0045 - accuracy: 0.9985 - val_loss: 0.1114 - val_accuracy: 0.9839
Epoch 69/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 0.1215 - val_accuracy: 0.9811
Epoch 70/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.1130 - val_accuracy: 0.9832
Epoch 71/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.1248 - val_accuracy: 0.9831
Epoch 72/500
469/469 [==============================] - 2s 4ms/step - loss: 8.6865e-04 - accuracy: 0.9998 - val_loss: 0.1292 - val_accuracy: 0.9833
Epoch 73/500
469/469 [==============================] - 2s 4ms/step - loss: 3.3745e-04 - accuracy: 0.9999 - val_loss: 0.1524 - val_accuracy: 0.9828
Epoch 74/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0063 - accuracy: 0.9984 - val_loss: 0.1209 - val_accuracy: 0.9816
Epoch 75/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.1049 - val_accuracy: 0.9837
Epoch 76/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 0.1192 - val_accuracy: 0.9842
Epoch 77/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.1078 - val_accuracy: 0.9834
Epoch 78/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 0.1049 - val_accuracy: 0.9845
Epoch 79/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 0.1463 - val_accuracy: 0.9806
Epoch 80/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0020 - accuracy: 0.9996 - val_loss: 0.1152 - val_accuracy: 0.9827
Epoch 81/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 0.1263 - val_accuracy: 0.9816
Epoch 82/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1276 - val_accuracy: 0.9814
Epoch 83/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1174 - val_accuracy: 0.9835
Epoch 84/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1194 - val_accuracy: 0.9838
Epoch 85/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.1220 - val_accuracy: 0.9844
Epoch 86/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.1310 - val_accuracy: 0.9828
Epoch 87/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0054 - accuracy: 0.9987 - val_loss: 0.1514 - val_accuracy: 0.9784
Epoch 88/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.1030 - val_accuracy: 0.9840
Epoch 89/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 0.1090 - val_accuracy: 0.9841
Epoch 90/500
469/469 [==============================] - 2s 4ms/step - loss: 8.4610e-04 - accuracy: 0.9998 - val_loss: 0.1194 - val_accuracy: 0.9837
Epoch 91/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 0.1230 - val_accuracy: 0.9844
Epoch 92/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.1356 - val_accuracy: 0.9839
Epoch 93/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0043 - accuracy: 0.9990 - val_loss: 0.1076 - val_accuracy: 0.9821
Epoch 94/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0031 - accuracy: 0.9992 - val_loss: 0.1012 - val_accuracy: 0.9852
Epoch 95/500
469/469 [==============================] - 2s 4ms/step - loss: 8.1479e-04 - accuracy: 0.9998 - val_loss: 0.1112 - val_accuracy: 0.9856
Epoch 96/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5384e-04 - accuracy: 0.9999 - val_loss: 0.1177 - val_accuracy: 0.9857
Epoch 97/500
469/469 [==============================] - 2s 4ms/step - loss: 9.3516e-06 - accuracy: 1.0000 - val_loss: 0.1214 - val_accuracy: 0.9857
Epoch 98/500
469/469 [==============================] - 2s 4ms/step - loss: 4.5289e-06 - accuracy: 1.0000 - val_loss: 0.1248 - val_accuracy: 0.9857
Epoch 99/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0145e-06 - accuracy: 1.0000 - val_loss: 0.1279 - val_accuracy: 0.9856
Epoch 100/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1782e-06 - accuracy: 1.0000 - val_loss: 0.1307 - val_accuracy: 0.9856
Epoch 101/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6233e-06 - accuracy: 1.0000 - val_loss: 0.1334 - val_accuracy: 0.9856
Epoch 102/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2253e-06 - accuracy: 1.0000 - val_loss: 0.1359 - val_accuracy: 0.9856
Epoch 103/500
469/469 [==============================] - 2s 4ms/step - loss: 9.3665e-07 - accuracy: 1.0000 - val_loss: 0.1382 - val_accuracy: 0.9855
Epoch 104/500
469/469 [==============================] - 2s 4ms/step - loss: 7.3005e-07 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9854
Epoch 105/500
469/469 [==============================] - 2s 4ms/step - loss: 5.6435e-07 - accuracy: 1.0000 - val_loss: 0.1428 - val_accuracy: 0.9854
Epoch 106/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4435e-07 - accuracy: 1.0000 - val_loss: 0.1449 - val_accuracy: 0.9854
Epoch 107/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5198e-07 - accuracy: 1.0000 - val_loss: 0.1470 - val_accuracy: 0.9854
Epoch 108/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7838e-07 - accuracy: 1.0000 - val_loss: 0.1490 - val_accuracy: 0.9854
Epoch 109/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2501e-07 - accuracy: 1.0000 - val_loss: 0.1509 - val_accuracy: 0.9854
Epoch 110/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8387e-07 - accuracy: 1.0000 - val_loss: 0.1527 - val_accuracy: 0.9854
Epoch 111/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5080e-07 - accuracy: 1.0000 - val_loss: 0.1545 - val_accuracy: 0.9855
Epoch 112/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2090e-07 - accuracy: 1.0000 - val_loss: 0.1563 - val_accuracy: 0.9855
Epoch 113/500
469/469 [==============================] - 2s 4ms/step - loss: 9.4881e-08 - accuracy: 1.0000 - val_loss: 0.1579 - val_accuracy: 0.9855
Epoch 114/500
469/469 [==============================] - 2s 4ms/step - loss: 7.6890e-08 - accuracy: 1.0000 - val_loss: 0.1595 - val_accuracy: 0.9856
Epoch 115/500
469/469 [==============================] - 2s 4ms/step - loss: 6.3334e-08 - accuracy: 1.0000 - val_loss: 0.1610 - val_accuracy: 0.9857
Epoch 116/500
469/469 [==============================] - 2s 4ms/step - loss: 5.2881e-08 - accuracy: 1.0000 - val_loss: 0.1625 - val_accuracy: 0.9857
Epoch 117/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4438e-08 - accuracy: 1.0000 - val_loss: 0.1639 - val_accuracy: 0.9857
Epoch 118/500
469/469 [==============================] - 2s 4ms/step - loss: 3.7847e-08 - accuracy: 1.0000 - val_loss: 0.1652 - val_accuracy: 0.9857
Epoch 119/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2166e-08 - accuracy: 1.0000 - val_loss: 0.1665 - val_accuracy: 0.9857
Epoch 120/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7555e-08 - accuracy: 1.0000 - val_loss: 0.1677 - val_accuracy: 0.9857
Epoch 121/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3654e-08 - accuracy: 1.0000 - val_loss: 0.1689 - val_accuracy: 0.9857
Epoch 122/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0280e-08 - accuracy: 1.0000 - val_loss: 0.1700 - val_accuracy: 0.9857
Epoch 123/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7577e-08 - accuracy: 1.0000 - val_loss: 0.1711 - val_accuracy: 0.9857
Epoch 124/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5213e-08 - accuracy: 1.0000 - val_loss: 0.1721 - val_accuracy: 0.9858
Epoch 125/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3274e-08 - accuracy: 1.0000 - val_loss: 0.1732 - val_accuracy: 0.9858
Epoch 126/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1646e-08 - accuracy: 1.0000 - val_loss: 0.1741 - val_accuracy: 0.9858
Epoch 127/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0288e-08 - accuracy: 1.0000 - val_loss: 0.1750 - val_accuracy: 0.9858
Epoch 128/500
469/469 [==============================] - 2s 4ms/step - loss: 9.1193e-09 - accuracy: 1.0000 - val_loss: 0.1759 - val_accuracy: 0.9858
Epoch 129/500
469/469 [==============================] - 2s 4ms/step - loss: 8.1141e-09 - accuracy: 1.0000 - val_loss: 0.1767 - val_accuracy: 0.9858
Epoch 130/500
469/469 [==============================] - 2s 4ms/step - loss: 7.2995e-09 - accuracy: 1.0000 - val_loss: 0.1775 - val_accuracy: 0.9859
Epoch 131/500
469/469 [==============================] - 2s 4ms/step - loss: 6.5405e-09 - accuracy: 1.0000 - val_loss: 0.1783 - val_accuracy: 0.9859
Epoch 132/500
469/469 [==============================] - 2s 4ms/step - loss: 5.9048e-09 - accuracy: 1.0000 - val_loss: 0.1790 - val_accuracy: 0.9860
Epoch 133/500
469/469 [==============================] - 2s 4ms/step - loss: 5.3723e-09 - accuracy: 1.0000 - val_loss: 0.1797 - val_accuracy: 0.9860
Epoch 134/500
469/469 [==============================] - 2s 4ms/step - loss: 4.8875e-09 - accuracy: 1.0000 - val_loss: 0.1804 - val_accuracy: 0.9860
Epoch 135/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4485e-09 - accuracy: 1.0000 - val_loss: 0.1810 - val_accuracy: 0.9859
Epoch 136/500
469/469 [==============================] - 2s 4ms/step - loss: 4.1326e-09 - accuracy: 1.0000 - val_loss: 0.1816 - val_accuracy: 0.9859
Epoch 137/500
469/469 [==============================] - 2s 4ms/step - loss: 3.8286e-09 - accuracy: 1.0000 - val_loss: 0.1822 - val_accuracy: 0.9859
Epoch 138/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5266e-09 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9859
Epoch 139/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2822e-09 - accuracy: 1.0000 - val_loss: 0.1833 - val_accuracy: 0.9858
Epoch 140/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0637e-09 - accuracy: 1.0000 - val_loss: 0.1838 - val_accuracy: 0.9858
Epoch 141/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8749e-09 - accuracy: 1.0000 - val_loss: 0.1842 - val_accuracy: 0.9858
Epoch 142/500
469/469 [==============================] - 2s 4ms/step - loss: 2.6961e-09 - accuracy: 1.0000 - val_loss: 0.1846 - val_accuracy: 0.9858
Epoch 143/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.1850 - val_accuracy: 0.9858
Epoch 144/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9858
Epoch 145/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2987e-09 - accuracy: 1.0000 - val_loss: 0.1858 - val_accuracy: 0.9859
Epoch 146/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1895e-09 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9859
Epoch 147/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0742e-09 - accuracy: 1.0000 - val_loss: 0.1866 - val_accuracy: 0.9859
Epoch 148/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9749e-09 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9859
Epoch 149/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9014e-09 - accuracy: 1.0000 - val_loss: 0.1872 - val_accuracy: 0.9859
Epoch 150/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8159e-09 - accuracy: 1.0000 - val_loss: 0.1875 - val_accuracy: 0.9859
Epoch 151/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7484e-09 - accuracy: 1.0000 - val_loss: 0.1878 - val_accuracy: 0.9859
Epoch 152/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6908e-09 - accuracy: 1.0000 - val_loss: 0.1881 - val_accuracy: 0.9859
Epoch 153/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6312e-09 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9859
Epoch 154/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5557e-09 - accuracy: 1.0000 - val_loss: 0.1887 - val_accuracy: 0.9859
Epoch 155/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5120e-09 - accuracy: 1.0000 - val_loss: 0.1889 - val_accuracy: 0.9859
Epoch 156/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4583e-09 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9859
Epoch 157/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4206e-09 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9859
Epoch 158/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3689e-09 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9859
Epoch 159/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3252e-09 - accuracy: 1.0000 - val_loss: 0.1899 - val_accuracy: 0.9859
Epoch 160/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2855e-09 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9859
Epoch 161/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2457e-09 - accuracy: 1.0000 - val_loss: 0.1904 - val_accuracy: 0.9859
Epoch 162/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1981e-09 - accuracy: 1.0000 - val_loss: 0.1906 - val_accuracy: 0.9859
Epoch 163/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1802e-09 - accuracy: 1.0000 - val_loss: 0.1908 - val_accuracy: 0.9859
Epoch 164/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1404e-09 - accuracy: 1.0000 - val_loss: 0.1910 - val_accuracy: 0.9859
Epoch 165/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1265e-09 - accuracy: 1.0000 - val_loss: 0.1912 - val_accuracy: 0.9859
Epoch 166/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0808e-09 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9859
Epoch 167/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0510e-09 - accuracy: 1.0000 - val_loss: 0.1915 - val_accuracy: 0.9859
Epoch 168/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0312e-09 - accuracy: 1.0000 - val_loss: 0.1917 - val_accuracy: 0.9859
Epoch 169/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0014e-09 - accuracy: 1.0000 - val_loss: 0.1919 - val_accuracy: 0.9859
Epoch 170/500
469/469 [==============================] - 2s 4ms/step - loss: 9.7751e-10 - accuracy: 1.0000 - val_loss: 0.1921 - val_accuracy: 0.9859
Epoch 171/500
469/469 [==============================] - 2s 4ms/step - loss: 9.4970e-10 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9859
Epoch 172/500
469/469 [==============================] - 2s 4ms/step - loss: 9.2983e-10 - accuracy: 1.0000 - val_loss: 0.1924 - val_accuracy: 0.9859
Epoch 173/500
469/469 [==============================] - 2s 4ms/step - loss: 9.0400e-10 - accuracy: 1.0000 - val_loss: 0.1926 - val_accuracy: 0.9859
Epoch 174/500
469/469 [==============================] - 2s 4ms/step - loss: 8.8215e-10 - accuracy: 1.0000 - val_loss: 0.1927 - val_accuracy: 0.9859
Epoch 175/500
469/469 [==============================] - 2s 4ms/step - loss: 8.6228e-10 - accuracy: 1.0000 - val_loss: 0.1929 - val_accuracy: 0.9859
Epoch 176/500
469/469 [==============================] - 2s 4ms/step - loss: 8.4440e-10 - accuracy: 1.0000 - val_loss: 0.1930 - val_accuracy: 0.9860
Epoch 177/500
469/469 [==============================] - 2s 4ms/step - loss: 8.2850e-10 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9860
Epoch 178/500
469/469 [==============================] - 2s 4ms/step - loss: 8.0665e-10 - accuracy: 1.0000 - val_loss: 0.1933 - val_accuracy: 0.9860
Epoch 179/500
469/469 [==============================] - 2s 4ms/step - loss: 7.8281e-10 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9860
Epoch 180/500
469/469 [==============================] - 2s 4ms/step - loss: 7.7287e-10 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9860
Epoch 181/500
469/469 [==============================] - 2s 4ms/step - loss: 7.6095e-10 - accuracy: 1.0000 - val_loss: 0.1937 - val_accuracy: 0.9860
Epoch 182/500
469/469 [==============================] - 2s 4ms/step - loss: 7.4704e-10 - accuracy: 1.0000 - val_loss: 0.1939 - val_accuracy: 0.9860
Epoch 183/500
469/469 [==============================] - 2s 4ms/step - loss: 7.3314e-10 - accuracy: 1.0000 - val_loss: 0.1940 - val_accuracy: 0.9860
Epoch 184/500
469/469 [==============================] - 2s 4ms/step - loss: 7.1327e-10 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9860
Epoch 185/500
469/469 [==============================] - 2s 4ms/step - loss: 7.0333e-10 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9860
Epoch 186/500
469/469 [==============================] - 2s 4ms/step - loss: 6.8545e-10 - accuracy: 1.0000 - val_loss: 0.1944 - val_accuracy: 0.9860
Epoch 187/500
469/469 [==============================] - 2s 4ms/step - loss: 6.7353e-10 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9860
Epoch 188/500
469/469 [==============================] - 2s 4ms/step - loss: 6.6161e-10 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9860
Epoch 189/500
469/469 [==============================] - 2s 4ms/step - loss: 6.4770e-10 - accuracy: 1.0000 - val_loss: 0.1947 - val_accuracy: 0.9861
Epoch 190/500
469/469 [==============================] - 2s 5ms/step - loss: 6.3380e-10 - accuracy: 1.0000 - val_loss: 0.1948 - val_accuracy: 0.9861
Epoch 191/500
469/469 [==============================] - 2s 5ms/step - loss: 6.1989e-10 - accuracy: 1.0000 - val_loss: 0.1949 - val_accuracy: 0.9861
Epoch 192/500
469/469 [==============================] - 2s 5ms/step - loss: 6.0995e-10 - accuracy: 1.0000 - val_loss: 0.1950 - val_accuracy: 0.9861
Epoch 193/500
469/469 [==============================] - 2s 4ms/step - loss: 6.0399e-10 - accuracy: 1.0000 - val_loss: 0.1952 - val_accuracy: 0.9861
Epoch 194/500
469/469 [==============================] - 2s 4ms/step - loss: 6.0002e-10 - accuracy: 1.0000 - val_loss: 0.1953 - val_accuracy: 0.9861
Epoch 195/500
469/469 [==============================] - 2s 4ms/step - loss: 5.9009e-10 - accuracy: 1.0000 - val_loss: 0.1954 - val_accuracy: 0.9861
Epoch 196/500
469/469 [==============================] - 2s 4ms/step - loss: 5.8611e-10 - accuracy: 1.0000 - val_loss: 0.1955 - val_accuracy: 0.9861
Epoch 197/500
469/469 [==============================] - 2s 4ms/step - loss: 5.8412e-10 - accuracy: 1.0000 - val_loss: 0.1956 - val_accuracy: 0.9861
Epoch 198/500
469/469 [==============================] - 2s 4ms/step - loss: 5.7419e-10 - accuracy: 1.0000 - val_loss: 0.1957 - val_accuracy: 0.9861
Epoch 199/500
469/469 [==============================] - 2s 4ms/step - loss: 5.6227e-10 - accuracy: 1.0000 - val_loss: 0.1958 - val_accuracy: 0.9861
Epoch 200/500
469/469 [==============================] - 2s 4ms/step - loss: 5.5432e-10 - accuracy: 1.0000 - val_loss: 0.1959 - val_accuracy: 0.9861
Epoch 201/500
469/469 [==============================] - 2s 4ms/step - loss: 5.4439e-10 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9861
Epoch 202/500
469/469 [==============================] - 2s 4ms/step - loss: 5.3445e-10 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9861
Epoch 203/500
469/469 [==============================] - 2s 4ms/step - loss: 5.3048e-10 - accuracy: 1.0000 - val_loss: 0.1962 - val_accuracy: 0.9861
Epoch 204/500
469/469 [==============================] - 2s 4ms/step - loss: 5.2452e-10 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9861
Epoch 205/500
469/469 [==============================] - 2s 4ms/step - loss: 5.1260e-10 - accuracy: 1.0000 - val_loss: 0.1964 - val_accuracy: 0.9861
Epoch 206/500
469/469 [==============================] - 2s 4ms/step - loss: 5.0267e-10 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9861
Epoch 207/500
469/469 [==============================] - 2s 4ms/step - loss: 4.9670e-10 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9861
Epoch 208/500
469/469 [==============================] - 2s 4ms/step - loss: 4.9074e-10 - accuracy: 1.0000 - val_loss: 0.1966 - val_accuracy: 0.9861
Epoch 209/500
469/469 [==============================] - 2s 4ms/step - loss: 4.8280e-10 - accuracy: 1.0000 - val_loss: 0.1967 - val_accuracy: 0.9861
Epoch 210/500
469/469 [==============================] - 2s 4ms/step - loss: 4.7882e-10 - accuracy: 1.0000 - val_loss: 0.1968 - val_accuracy: 0.9861
Epoch 211/500
469/469 [==============================] - 2s 4ms/step - loss: 4.7684e-10 - accuracy: 1.0000 - val_loss: 0.1969 - val_accuracy: 0.9861
Epoch 212/500
469/469 [==============================] - 2s 4ms/step - loss: 4.6889e-10 - accuracy: 1.0000 - val_loss: 0.1970 - val_accuracy: 0.9861
Epoch 213/500
469/469 [==============================] - 2s 4ms/step - loss: 4.6492e-10 - accuracy: 1.0000 - val_loss: 0.1971 - val_accuracy: 0.9861
Epoch 214/500
469/469 [==============================] - 2s 4ms/step - loss: 4.5896e-10 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9861
Epoch 215/500
469/469 [==============================] - 2s 4ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.1972 - val_accuracy: 0.9861
Epoch 216/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4902e-10 - accuracy: 1.0000 - val_loss: 0.1973 - val_accuracy: 0.9861
Epoch 217/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4703e-10 - accuracy: 1.0000 - val_loss: 0.1974 - val_accuracy: 0.9861
Epoch 218/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4306e-10 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9861
Epoch 219/500
469/469 [==============================] - 2s 4ms/step - loss: 4.3511e-10 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9861
Epoch 220/500
469/469 [==============================] - 2s 4ms/step - loss: 4.2717e-10 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9861
Epoch 221/500
469/469 [==============================] - 2s 4ms/step - loss: 4.2121e-10 - accuracy: 1.0000 - val_loss: 0.1977 - val_accuracy: 0.9862
Epoch 222/500
469/469 [==============================] - 2s 4ms/step - loss: 4.2121e-10 - accuracy: 1.0000 - val_loss: 0.1978 - val_accuracy: 0.9862
Epoch 223/500
469/469 [==============================] - 2s 4ms/step - loss: 4.1127e-10 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9862
Epoch 224/500
469/469 [==============================] - 2s 4ms/step - loss: 4.0730e-10 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9862
Epoch 225/500
469/469 [==============================] - 2s 4ms/step - loss: 4.0531e-10 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9862
Epoch 226/500
469/469 [==============================] - 2s 4ms/step - loss: 4.0134e-10 - accuracy: 1.0000 - val_loss: 0.1981 - val_accuracy: 0.9862
Epoch 227/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9538e-10 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9862
Epoch 228/500
469/469 [==============================] - 2s 4ms/step - loss: 3.8942e-10 - accuracy: 1.0000 - val_loss: 0.1982 - val_accuracy: 0.9862
Epoch 229/500
469/469 [==============================] - 2s 4ms/step - loss: 3.8346e-10 - accuracy: 1.0000 - val_loss: 0.1983 - val_accuracy: 0.9862
Epoch 230/500
469/469 [==============================] - 2s 4ms/step - loss: 3.8346e-10 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9862
Epoch 231/500
469/469 [==============================] - 2s 4ms/step - loss: 3.7750e-10 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9862
Epoch 232/500
469/469 [==============================] - 2s 4ms/step - loss: 3.7551e-10 - accuracy: 1.0000 - val_loss: 0.1985 - val_accuracy: 0.9862
Epoch 233/500
469/469 [==============================] - 2s 4ms/step - loss: 3.7352e-10 - accuracy: 1.0000 - val_loss: 0.1986 - val_accuracy: 0.9862
Epoch 234/500
469/469 [==============================] - 2s 4ms/step - loss: 3.6557e-10 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9862
Epoch 235/500
469/469 [==============================] - 2s 4ms/step - loss: 3.6359e-10 - accuracy: 1.0000 - val_loss: 0.1987 - val_accuracy: 0.9862
Epoch 236/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5763e-10 - accuracy: 1.0000 - val_loss: 0.1988 - val_accuracy: 0.9862
Epoch 237/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5365e-10 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9862
Epoch 238/500
469/469 [==============================] - 2s 4ms/step - loss: 3.4968e-10 - accuracy: 1.0000 - val_loss: 0.1989 - val_accuracy: 0.9862
Epoch 239/500
469/469 [==============================] - 2s 4ms/step - loss: 3.4968e-10 - accuracy: 1.0000 - val_loss: 0.1990 - val_accuracy: 0.9862
Epoch 240/500
469/469 [==============================] - 2s 4ms/step - loss: 3.4372e-10 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9862
Epoch 241/500
469/469 [==============================] - 2s 4ms/step - loss: 3.4173e-10 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9862
Epoch 242/500
469/469 [==============================] - 2s 4ms/step - loss: 3.4173e-10 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9862
Epoch 243/500
469/469 [==============================] - 2s 4ms/step - loss: 3.3776e-10 - accuracy: 1.0000 - val_loss: 0.1992 - val_accuracy: 0.9862
Epoch 244/500
469/469 [==============================] - 2s 4ms/step - loss: 3.3577e-10 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9862
Epoch 245/500
469/469 [==============================] - 2s 4ms/step - loss: 3.3379e-10 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9862
Epoch 246/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2981e-10 - accuracy: 1.0000 - val_loss: 0.1994 - val_accuracy: 0.9862
Epoch 247/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2783e-10 - accuracy: 1.0000 - val_loss: 0.1995 - val_accuracy: 0.9862
Epoch 248/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2385e-10 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9862
Epoch 249/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2186e-10 - accuracy: 1.0000 - val_loss: 0.1996 - val_accuracy: 0.9862
Epoch 250/500
469/469 [==============================] - 2s 4ms/step - loss: 3.1988e-10 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9862
Epoch 251/500
469/469 [==============================] - 2s 4ms/step - loss: 3.1988e-10 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9862
Epoch 252/500
469/469 [==============================] - 2s 4ms/step - loss: 3.1988e-10 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9862
Epoch 253/500
469/469 [==============================] - 2s 4ms/step - loss: 3.1392e-10 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9862
Epoch 254/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0994e-10 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9862
Epoch 255/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0994e-10 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9862
Epoch 256/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0398e-10 - accuracy: 1.0000 - val_loss: 0.2000 - val_accuracy: 0.9862
Epoch 257/500
469/469 [==============================] - 2s 4ms/step - loss: 3.0001e-10 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9862
Epoch 258/500
469/469 [==============================] - 2s 4ms/step - loss: 2.9405e-10 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9862
Epoch 259/500
469/469 [==============================] - 2s 4ms/step - loss: 2.9206e-10 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9862
Epoch 260/500
469/469 [==============================] - 2s 4ms/step - loss: 2.9008e-10 - accuracy: 1.0000 - val_loss: 0.2002 - val_accuracy: 0.9862
Epoch 261/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8809e-10 - accuracy: 1.0000 - val_loss: 0.2003 - val_accuracy: 0.9862
Epoch 262/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8610e-10 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9862
Epoch 263/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8412e-10 - accuracy: 1.0000 - val_loss: 0.2004 - val_accuracy: 0.9862
Epoch 264/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8213e-10 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9862
Epoch 265/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7815e-10 - accuracy: 1.0000 - val_loss: 0.2005 - val_accuracy: 0.9862
Epoch 266/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7815e-10 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9862
Epoch 267/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7815e-10 - accuracy: 1.0000 - val_loss: 0.2006 - val_accuracy: 0.9862
Epoch 268/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7815e-10 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9862
Epoch 269/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7418e-10 - accuracy: 1.0000 - val_loss: 0.2007 - val_accuracy: 0.9862
Epoch 270/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7021e-10 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9862
Epoch 271/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7021e-10 - accuracy: 1.0000 - val_loss: 0.2008 - val_accuracy: 0.9862
Epoch 272/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7021e-10 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9862
Epoch 273/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7021e-10 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.9862
Epoch 274/500
469/469 [==============================] - 2s 4ms/step - loss: 2.6822e-10 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9862
Epoch 275/500
469/469 [==============================] - 2s 4ms/step - loss: 2.6425e-10 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9862
Epoch 276/500
469/469 [==============================] - 2s 4ms/step - loss: 2.6425e-10 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9862
Epoch 277/500
469/469 [==============================] - 2s 4ms/step - loss: 2.6226e-10 - accuracy: 1.0000 - val_loss: 0.2011 - val_accuracy: 0.9862
Epoch 278/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5431e-10 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9862
Epoch 279/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5233e-10 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9862
Epoch 280/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5034e-10 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9862
Epoch 281/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4835e-10 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9862
Epoch 282/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4438e-10 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9862
Epoch 283/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4438e-10 - accuracy: 1.0000 - val_loss: 0.2014 - val_accuracy: 0.9862
Epoch 284/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4239e-10 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9862
Epoch 285/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4239e-10 - accuracy: 1.0000 - val_loss: 0.2015 - val_accuracy: 0.9862
Epoch 286/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4041e-10 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9862
Epoch 287/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4239e-10 - accuracy: 1.0000 - val_loss: 0.2016 - val_accuracy: 0.9862
Epoch 288/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3842e-10 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9862
Epoch 289/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3643e-10 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9862
Epoch 290/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3643e-10 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.9862
Epoch 291/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3444e-10 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9862
Epoch 292/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.2018 - val_accuracy: 0.9862
Epoch 293/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9862
Epoch 294/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.2019 - val_accuracy: 0.9862
Epoch 295/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9862
Epoch 296/500
469/469 [==============================] - 2s 4ms/step - loss: 2.3047e-10 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9862
Epoch 297/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2848e-10 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9862
Epoch 298/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2650e-10 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9862
Epoch 299/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2451e-10 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9862
Epoch 300/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2054e-10 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9862
Epoch 301/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2054e-10 - accuracy: 1.0000 - val_loss: 0.2022 - val_accuracy: 0.9862
Epoch 302/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2054e-10 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9862
Epoch 303/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2054e-10 - accuracy: 1.0000 - val_loss: 0.2023 - val_accuracy: 0.9862
Epoch 304/500
469/469 [==============================] - 2s 4ms/step - loss: 2.2054e-10 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9862
Epoch 305/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1656e-10 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9862
Epoch 306/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1458e-10 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9862
Epoch 307/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1458e-10 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9862
Epoch 308/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1259e-10 - accuracy: 1.0000 - val_loss: 0.2025 - val_accuracy: 0.9862
Epoch 309/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1259e-10 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9862
Epoch 310/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1060e-10 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9862
Epoch 311/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1060e-10 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9862
Epoch 312/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0862e-10 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9862
Epoch 313/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0663e-10 - accuracy: 1.0000 - val_loss: 0.2027 - val_accuracy: 0.9862
Epoch 314/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0663e-10 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9862
Epoch 315/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0464e-10 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9862
Epoch 316/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0464e-10 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9862
Epoch 317/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0464e-10 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9862
Epoch 318/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0464e-10 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9862
Epoch 319/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0464e-10 - accuracy: 1.0000 - val_loss: 0.2029 - val_accuracy: 0.9862
Epoch 320/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0067e-10 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9862
Epoch 321/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0067e-10 - accuracy: 1.0000 - val_loss: 0.2030 - val_accuracy: 0.9862
Epoch 322/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-10 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9862
Epoch 323/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-10 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9862
Epoch 324/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9272e-10 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9862
Epoch 325/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9073e-10 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9862
Epoch 326/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8875e-10 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9862
Epoch 327/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8875e-10 - accuracy: 1.0000 - val_loss: 0.2032 - val_accuracy: 0.9862
Epoch 328/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8676e-10 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9862
Epoch 329/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8676e-10 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9862
Epoch 330/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8477e-10 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9862
Epoch 331/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8477e-10 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9862
Epoch 332/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8477e-10 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9862
Epoch 333/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8477e-10 - accuracy: 1.0000 - val_loss: 0.2034 - val_accuracy: 0.9862
Epoch 334/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8279e-10 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9862
Epoch 335/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8279e-10 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9862
Epoch 336/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7881e-10 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9862
Epoch 337/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7881e-10 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9862
Epoch 338/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7881e-10 - accuracy: 1.0000 - val_loss: 0.2036 - val_accuracy: 0.9862
Epoch 339/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7683e-10 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9862
Epoch 340/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7484e-10 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9862
Epoch 341/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7484e-10 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9862
Epoch 342/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7484e-10 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9862
Epoch 343/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7285e-10 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9862
Epoch 344/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7285e-10 - accuracy: 1.0000 - val_loss: 0.2038 - val_accuracy: 0.9862
Epoch 345/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7087e-10 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9862
Epoch 346/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6888e-10 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9862
Epoch 347/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6292e-10 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9862
Epoch 348/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9862
Epoch 349/500
469/469 [==============================] - 2s 5ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9862
Epoch 350/500
469/469 [==============================] - 2s 5ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9862
Epoch 351/500
469/469 [==============================] - 2s 5ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9862
Epoch 352/500
469/469 [==============================] - 2s 5ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9862
Epoch 353/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9862
Epoch 354/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2041 - val_accuracy: 0.9862
Epoch 355/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9862
Epoch 356/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9862
Epoch 357/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2042 - val_accuracy: 0.9862
Epoch 358/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9862
Epoch 359/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6093e-10 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9862
Epoch 360/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2043 - val_accuracy: 0.9862
Epoch 361/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9862
Epoch 362/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9862
Epoch 363/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9862
Epoch 364/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9862
Epoch 365/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9862
Epoch 366/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5696e-10 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9862
Epoch 367/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5497e-10 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9862
Epoch 368/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5497e-10 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9862
Epoch 369/500
469/469 [==============================] - 2s 4ms/step - loss: 1.5100e-10 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9862
Epoch 370/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9862
Epoch 371/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9862
Epoch 372/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9862
Epoch 373/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9862
Epoch 374/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9862
Epoch 375/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4702e-10 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9862
Epoch 376/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4504e-10 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9862
Epoch 377/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4504e-10 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9862
Epoch 378/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4504e-10 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9862
Epoch 379/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9862
Epoch 380/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9862
Epoch 381/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9862
Epoch 382/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9862
Epoch 383/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9862
Epoch 384/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4305e-10 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9862
Epoch 385/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4106e-10 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9862
Epoch 386/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3908e-10 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9862
Epoch 387/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3709e-10 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9862
Epoch 388/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3709e-10 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9862
Epoch 389/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3709e-10 - accuracy: 1.0000 - val_loss: 0.2051 - val_accuracy: 0.9862
Epoch 390/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3510e-10 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9862
Epoch 391/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3510e-10 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9862
Epoch 392/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3510e-10 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9862
Epoch 393/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3312e-10 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9862
Epoch 394/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3312e-10 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9862
Epoch 395/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3312e-10 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9862
Epoch 396/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3312e-10 - accuracy: 1.0000 - val_loss: 0.2053 - val_accuracy: 0.9862
Epoch 397/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3113e-10 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9862
Epoch 398/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3113e-10 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9862
Epoch 399/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9862
Epoch 400/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2054 - val_accuracy: 0.9862
Epoch 401/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9862
Epoch 402/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9862
Epoch 403/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9862
Epoch 404/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2055 - val_accuracy: 0.9862
Epoch 405/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9862
Epoch 406/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9862
Epoch 407/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2716e-10 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9862
Epoch 408/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2056 - val_accuracy: 0.9862
Epoch 409/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2517e-10 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9862
Epoch 410/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2517e-10 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9862
Epoch 411/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2318e-10 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9862
Epoch 412/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2318e-10 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9862
Epoch 413/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2318e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9862
Epoch 414/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9862
Epoch 415/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9862
Epoch 416/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9862
Epoch 417/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9862
Epoch 418/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9862
Epoch 419/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9862
Epoch 420/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9862
Epoch 421/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9862
Epoch 422/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9862
Epoch 423/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9862
Epoch 424/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9862
Epoch 425/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9862
Epoch 426/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9862
Epoch 427/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9862
Epoch 428/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9862
Epoch 429/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9862
Epoch 430/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9862
Epoch 431/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2120e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9862
Epoch 432/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1921e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9862
Epoch 433/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1722e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9862
Epoch 434/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1524e-10 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9862
Epoch 435/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1325e-10 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9862
Epoch 436/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1325e-10 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9862
Epoch 437/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1325e-10 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9862
Epoch 438/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9862
Epoch 439/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9862
Epoch 440/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9862
Epoch 441/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9862
Epoch 442/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9862
Epoch 443/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9862
Epoch 444/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9862
Epoch 445/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9862
Epoch 446/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9862
Epoch 447/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9862
Epoch 448/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9862
Epoch 449/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9862
Epoch 450/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9862
Epoch 451/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9862
Epoch 452/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9862
Epoch 453/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9862
Epoch 454/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9862
Epoch 455/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9862
Epoch 456/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0928e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9862
Epoch 457/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0928e-10 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9862
Epoch 458/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0928e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9861
Epoch 459/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0729e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9861
Epoch 460/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9861
Epoch 461/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9861
Epoch 462/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9861
Epoch 463/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9861
Epoch 464/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9861
Epoch 465/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9861
Epoch 466/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0530e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9861
Epoch 467/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9861
Epoch 468/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9861
Epoch 469/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9861
Epoch 470/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9861
Epoch 471/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9861
Epoch 472/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2070 - val_accuracy: 0.9861
Epoch 473/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9861
Epoch 474/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0331e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9861
Epoch 475/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9861
Epoch 476/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9861
Epoch 477/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9861
Epoch 478/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9861
Epoch 479/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9861
Epoch 480/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9861
Epoch 481/500
469/469 [==============================] - 2s 4ms/step - loss: 1.0133e-10 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9861
Epoch 482/500
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-11 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9861
Epoch 483/500
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 484/500
469/469 [==============================] - 2s 4ms/step - loss: 9.9341e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 485/500
469/469 [==============================] - 2s 4ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 486/500
469/469 [==============================] - 2s 4ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 487/500
469/469 [==============================] - 2s 4ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 488/500
469/469 [==============================] - 2s 4ms/step - loss: 9.7354e-11 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9861
Epoch 489/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9861
Epoch 490/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9861
Epoch 491/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9861
Epoch 492/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9861
Epoch 493/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9861
Epoch 494/500
469/469 [==============================] - 2s 4ms/step - loss: 9.5367e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 495/500
469/469 [==============================] - 2s 4ms/step - loss: 9.3381e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 496/500
469/469 [==============================] - 2s 4ms/step - loss: 9.3381e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 497/500
469/469 [==============================] - 2s 4ms/step - loss: 9.3381e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 498/500
469/469 [==============================] - 2s 4ms/step - loss: 9.1394e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 499/500
469/469 [==============================] - 2s 4ms/step - loss: 9.1394e-11 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9861
Epoch 500/500
469/469 [==============================] - 2s 4ms/step - loss: 9.1394e-11 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9861

Activation function: the logistic sigmoid function; initialization: Xavier initializer; with dropout rate: 0.2 for the first layer and 0.5 for the other hidden layers

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.GlorotNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='sigmoid',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_2 (Flatten)          (None, 784)               0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 784)               0         
_________________________________________________________________
dense_12 (Dense)             (None, 1024)              803840    
_________________________________________________________________
dropout_7 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_13 (Dense)             (None, 1024)              1049600   
_________________________________________________________________
dropout_8 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_14 (Dense)             (None, 1024)              1049600   
_________________________________________________________________
dropout_9 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_15 (Dense)             (None, 1024)              1049600   
_________________________________________________________________
dropout_10 (Dropout)         (None, 1024)              0         
_________________________________________________________________
dense_16 (Dense)             (None, 1024)              1049600   
_________________________________________________________________
dense_17 (Dense)             (None, 10)                10250     
=================================================================
Total params: 5,012,490
Trainable params: 5,012,490
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam()
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
historyd = model.fit(X_train, y_train, batch_size=128, epochs=500, validation_data=(X_test, y_test))
Epoch 1/500
469/469 [==============================] - 2s 5ms/step - loss: 1.1013 - accuracy: 0.6043 - val_loss: 0.3344 - val_accuracy: 0.8984
Epoch 2/500
469/469 [==============================] - 2s 4ms/step - loss: 0.3512 - accuracy: 0.8927 - val_loss: 0.2051 - val_accuracy: 0.9374
Epoch 3/500
469/469 [==============================] - 2s 4ms/step - loss: 0.2577 - accuracy: 0.9219 - val_loss: 0.1625 - val_accuracy: 0.9520
Epoch 4/500
469/469 [==============================] - 2s 4ms/step - loss: 0.2136 - accuracy: 0.9355 - val_loss: 0.1271 - val_accuracy: 0.9629
Epoch 5/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1861 - accuracy: 0.9447 - val_loss: 0.1187 - val_accuracy: 0.9653
Epoch 6/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1641 - accuracy: 0.9511 - val_loss: 0.1132 - val_accuracy: 0.9693
Epoch 7/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1524 - accuracy: 0.9538 - val_loss: 0.1031 - val_accuracy: 0.9699
Epoch 8/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1409 - accuracy: 0.9574 - val_loss: 0.0876 - val_accuracy: 0.9741
Epoch 9/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1251 - accuracy: 0.9627 - val_loss: 0.0936 - val_accuracy: 0.9740
Epoch 10/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1147 - accuracy: 0.9654 - val_loss: 0.0881 - val_accuracy: 0.9740
Epoch 11/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1119 - accuracy: 0.9659 - val_loss: 0.0810 - val_accuracy: 0.9764
Epoch 12/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1046 - accuracy: 0.9675 - val_loss: 0.0753 - val_accuracy: 0.9778
Epoch 13/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1007 - accuracy: 0.9694 - val_loss: 0.0731 - val_accuracy: 0.9783
Epoch 14/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0907 - accuracy: 0.9729 - val_loss: 0.0704 - val_accuracy: 0.9797
Epoch 15/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0894 - accuracy: 0.9726 - val_loss: 0.0702 - val_accuracy: 0.9800
Epoch 16/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - accuracy: 0.9737 - val_loss: 0.0686 - val_accuracy: 0.9801
Epoch 17/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - accuracy: 0.9758 - val_loss: 0.0705 - val_accuracy: 0.9811
Epoch 18/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0786 - accuracy: 0.9760 - val_loss: 0.0665 - val_accuracy: 0.9818
Epoch 19/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0757 - accuracy: 0.9771 - val_loss: 0.0637 - val_accuracy: 0.9820
Epoch 20/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0727 - accuracy: 0.9771 - val_loss: 0.0689 - val_accuracy: 0.9821
Epoch 21/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0698 - accuracy: 0.9791 - val_loss: 0.0697 - val_accuracy: 0.9810
Epoch 22/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0664 - accuracy: 0.9793 - val_loss: 0.0651 - val_accuracy: 0.9832
Epoch 23/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0628 - accuracy: 0.9808 - val_loss: 0.0693 - val_accuracy: 0.9826
Epoch 24/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0616 - accuracy: 0.9809 - val_loss: 0.0619 - val_accuracy: 0.9832
Epoch 25/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0578 - accuracy: 0.9825 - val_loss: 0.0642 - val_accuracy: 0.9837
Epoch 26/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0583 - accuracy: 0.9822 - val_loss: 0.0593 - val_accuracy: 0.9838
Epoch 27/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9827 - val_loss: 0.0607 - val_accuracy: 0.9835
Epoch 28/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0549 - accuracy: 0.9834 - val_loss: 0.0602 - val_accuracy: 0.9842
Epoch 29/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0530 - accuracy: 0.9838 - val_loss: 0.0587 - val_accuracy: 0.9850
Epoch 30/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0521 - accuracy: 0.9838 - val_loss: 0.0580 - val_accuracy: 0.9845
Epoch 31/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0507 - accuracy: 0.9842 - val_loss: 0.0573 - val_accuracy: 0.9854
Epoch 32/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9839 - val_loss: 0.0627 - val_accuracy: 0.9844
Epoch 33/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0503 - accuracy: 0.9846 - val_loss: 0.0554 - val_accuracy: 0.9862
Epoch 34/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0464 - accuracy: 0.9855 - val_loss: 0.0617 - val_accuracy: 0.9845
Epoch 35/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0471 - accuracy: 0.9855 - val_loss: 0.0586 - val_accuracy: 0.9856
Epoch 36/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0449 - accuracy: 0.9859 - val_loss: 0.0551 - val_accuracy: 0.9859
Epoch 37/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0452 - accuracy: 0.9861 - val_loss: 0.0558 - val_accuracy: 0.9860
Epoch 38/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0434 - accuracy: 0.9865 - val_loss: 0.0569 - val_accuracy: 0.9859
Epoch 39/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0446 - accuracy: 0.9864 - val_loss: 0.0585 - val_accuracy: 0.9843
Epoch 40/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0416 - accuracy: 0.9868 - val_loss: 0.0621 - val_accuracy: 0.9846
Epoch 41/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0421 - accuracy: 0.9866 - val_loss: 0.0608 - val_accuracy: 0.9852
Epoch 42/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0386 - accuracy: 0.9878 - val_loss: 0.0635 - val_accuracy: 0.9859
Epoch 43/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0383 - accuracy: 0.9880 - val_loss: 0.0626 - val_accuracy: 0.9857
Epoch 44/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0385 - accuracy: 0.9875 - val_loss: 0.0574 - val_accuracy: 0.9858
Epoch 45/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0368 - accuracy: 0.9882 - val_loss: 0.0618 - val_accuracy: 0.9865
Epoch 46/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0381 - accuracy: 0.9882 - val_loss: 0.0541 - val_accuracy: 0.9863
Epoch 47/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0359 - accuracy: 0.9891 - val_loss: 0.0599 - val_accuracy: 0.9856
Epoch 48/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0366 - accuracy: 0.9889 - val_loss: 0.0577 - val_accuracy: 0.9855
Epoch 49/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0336 - accuracy: 0.9899 - val_loss: 0.0603 - val_accuracy: 0.9851
Epoch 50/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0352 - accuracy: 0.9890 - val_loss: 0.0583 - val_accuracy: 0.9865
Epoch 51/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0350 - accuracy: 0.9894 - val_loss: 0.0586 - val_accuracy: 0.9862
Epoch 52/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0335 - accuracy: 0.9898 - val_loss: 0.0645 - val_accuracy: 0.9865
Epoch 53/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0343 - accuracy: 0.9895 - val_loss: 0.0581 - val_accuracy: 0.9867
Epoch 54/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0322 - accuracy: 0.9899 - val_loss: 0.0548 - val_accuracy: 0.9866
Epoch 55/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0337 - accuracy: 0.9890 - val_loss: 0.0555 - val_accuracy: 0.9867
Epoch 56/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0309 - accuracy: 0.9901 - val_loss: 0.0571 - val_accuracy: 0.9865
Epoch 57/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0314 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9854
Epoch 58/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0302 - accuracy: 0.9908 - val_loss: 0.0568 - val_accuracy: 0.9869
Epoch 59/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0306 - accuracy: 0.9903 - val_loss: 0.0562 - val_accuracy: 0.9872
Epoch 60/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0319 - accuracy: 0.9904 - val_loss: 0.0565 - val_accuracy: 0.9866
Epoch 61/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0291 - accuracy: 0.9906 - val_loss: 0.0598 - val_accuracy: 0.9862
Epoch 62/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0293 - accuracy: 0.9908 - val_loss: 0.0639 - val_accuracy: 0.9873
Epoch 63/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0287 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9863
Epoch 64/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0294 - accuracy: 0.9908 - val_loss: 0.0594 - val_accuracy: 0.9871
Epoch 65/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0293 - accuracy: 0.9913 - val_loss: 0.0565 - val_accuracy: 0.9866
Epoch 66/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0264 - accuracy: 0.9919 - val_loss: 0.0556 - val_accuracy: 0.9879
Epoch 67/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0289 - accuracy: 0.9910 - val_loss: 0.0555 - val_accuracy: 0.9863
Epoch 68/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0270 - accuracy: 0.9917 - val_loss: 0.0607 - val_accuracy: 0.9873
Epoch 69/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0274 - accuracy: 0.9919 - val_loss: 0.0572 - val_accuracy: 0.9866
Epoch 70/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0266 - accuracy: 0.9914 - val_loss: 0.0597 - val_accuracy: 0.9860
Epoch 71/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0278 - accuracy: 0.9912 - val_loss: 0.0592 - val_accuracy: 0.9872
Epoch 72/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0280 - accuracy: 0.9916 - val_loss: 0.0551 - val_accuracy: 0.9878
Epoch 73/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0250 - accuracy: 0.9922 - val_loss: 0.0610 - val_accuracy: 0.9877
Epoch 74/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0291 - accuracy: 0.9913 - val_loss: 0.0553 - val_accuracy: 0.9872
Epoch 75/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0243 - accuracy: 0.9926 - val_loss: 0.0604 - val_accuracy: 0.9877
Epoch 76/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0255 - accuracy: 0.9923 - val_loss: 0.0571 - val_accuracy: 0.9868
Epoch 77/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0260 - accuracy: 0.9919 - val_loss: 0.0569 - val_accuracy: 0.9869
Epoch 78/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0256 - accuracy: 0.9925 - val_loss: 0.0554 - val_accuracy: 0.9873
Epoch 79/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0248 - accuracy: 0.9927 - val_loss: 0.0550 - val_accuracy: 0.9884
Epoch 80/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0252 - accuracy: 0.9922 - val_loss: 0.0595 - val_accuracy: 0.9874
Epoch 81/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0257 - accuracy: 0.9916 - val_loss: 0.0654 - val_accuracy: 0.9868
Epoch 82/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0273 - accuracy: 0.9911 - val_loss: 0.0580 - val_accuracy: 0.9867
Epoch 83/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0248 - accuracy: 0.9926 - val_loss: 0.0614 - val_accuracy: 0.9870
Epoch 84/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0246 - accuracy: 0.9924 - val_loss: 0.0590 - val_accuracy: 0.9878
Epoch 85/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0241 - accuracy: 0.9927 - val_loss: 0.0525 - val_accuracy: 0.9874
Epoch 86/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0224 - accuracy: 0.9930 - val_loss: 0.0633 - val_accuracy: 0.9876
Epoch 87/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0253 - accuracy: 0.9922 - val_loss: 0.0570 - val_accuracy: 0.9877
Epoch 88/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0243 - accuracy: 0.9925 - val_loss: 0.0562 - val_accuracy: 0.9879
Epoch 89/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0239 - accuracy: 0.9927 - val_loss: 0.0537 - val_accuracy: 0.9887
Epoch 90/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0240 - accuracy: 0.9924 - val_loss: 0.0562 - val_accuracy: 0.9881
Epoch 91/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0218 - accuracy: 0.9931 - val_loss: 0.0528 - val_accuracy: 0.9889
Epoch 92/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0224 - accuracy: 0.9932 - val_loss: 0.0543 - val_accuracy: 0.9880
Epoch 93/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0238 - accuracy: 0.9929 - val_loss: 0.0528 - val_accuracy: 0.9882
Epoch 94/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0231 - accuracy: 0.9933 - val_loss: 0.0582 - val_accuracy: 0.9884
Epoch 95/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0232 - accuracy: 0.9927 - val_loss: 0.0584 - val_accuracy: 0.9880
Epoch 96/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0226 - accuracy: 0.9927 - val_loss: 0.0593 - val_accuracy: 0.9880
Epoch 97/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0216 - accuracy: 0.9936 - val_loss: 0.0587 - val_accuracy: 0.9881
Epoch 98/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0226 - accuracy: 0.9932 - val_loss: 0.0593 - val_accuracy: 0.9872
Epoch 99/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0211 - accuracy: 0.9934 - val_loss: 0.0612 - val_accuracy: 0.9869
Epoch 100/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0219 - accuracy: 0.9931 - val_loss: 0.0565 - val_accuracy: 0.9877
Epoch 101/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0227 - accuracy: 0.9928 - val_loss: 0.0551 - val_accuracy: 0.9882
Epoch 102/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0223 - accuracy: 0.9930 - val_loss: 0.0524 - val_accuracy: 0.9888
Epoch 103/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0235 - accuracy: 0.9933 - val_loss: 0.0535 - val_accuracy: 0.9882
Epoch 104/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0231 - accuracy: 0.9932 - val_loss: 0.0610 - val_accuracy: 0.9873
Epoch 105/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0212 - accuracy: 0.9934 - val_loss: 0.0553 - val_accuracy: 0.9888
Epoch 106/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0225 - accuracy: 0.9931 - val_loss: 0.0506 - val_accuracy: 0.9897
Epoch 107/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0235 - accuracy: 0.9932 - val_loss: 0.0560 - val_accuracy: 0.9882
Epoch 108/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0193 - accuracy: 0.9941 - val_loss: 0.0602 - val_accuracy: 0.9868
Epoch 109/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0202 - accuracy: 0.9938 - val_loss: 0.0621 - val_accuracy: 0.9874
Epoch 110/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0214 - accuracy: 0.9936 - val_loss: 0.0572 - val_accuracy: 0.9883
Epoch 111/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0194 - accuracy: 0.9937 - val_loss: 0.0628 - val_accuracy: 0.9880
Epoch 112/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0205 - accuracy: 0.9937 - val_loss: 0.0544 - val_accuracy: 0.9890
Epoch 113/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0192 - accuracy: 0.9941 - val_loss: 0.0560 - val_accuracy: 0.9885
Epoch 114/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0198 - accuracy: 0.9943 - val_loss: 0.0554 - val_accuracy: 0.9884
Epoch 115/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0222 - accuracy: 0.9932 - val_loss: 0.0575 - val_accuracy: 0.9886
Epoch 116/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9938 - val_loss: 0.0570 - val_accuracy: 0.9881
Epoch 117/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0192 - accuracy: 0.9941 - val_loss: 0.0621 - val_accuracy: 0.9872
Epoch 118/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0209 - accuracy: 0.9939 - val_loss: 0.0562 - val_accuracy: 0.9882
Epoch 119/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9943 - val_loss: 0.0597 - val_accuracy: 0.9875
Epoch 120/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0204 - accuracy: 0.9941 - val_loss: 0.0573 - val_accuracy: 0.9883
Epoch 121/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0205 - accuracy: 0.9939 - val_loss: 0.0606 - val_accuracy: 0.9878
Epoch 122/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0200 - accuracy: 0.9937 - val_loss: 0.0596 - val_accuracy: 0.9889
Epoch 123/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0190 - accuracy: 0.9945 - val_loss: 0.0598 - val_accuracy: 0.9875
Epoch 124/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0204 - accuracy: 0.9941 - val_loss: 0.0544 - val_accuracy: 0.9892
Epoch 125/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0195 - accuracy: 0.9943 - val_loss: 0.0543 - val_accuracy: 0.9892
Epoch 126/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0182 - accuracy: 0.9944 - val_loss: 0.0563 - val_accuracy: 0.9887
Epoch 127/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0193 - accuracy: 0.9940 - val_loss: 0.0565 - val_accuracy: 0.9881
Epoch 128/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.0602 - val_accuracy: 0.9868
Epoch 129/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0203 - accuracy: 0.9939 - val_loss: 0.0606 - val_accuracy: 0.9878
Epoch 130/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0550 - val_accuracy: 0.9889
Epoch 131/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0180 - accuracy: 0.9945 - val_loss: 0.0634 - val_accuracy: 0.9881
Epoch 132/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0176 - accuracy: 0.9949 - val_loss: 0.0592 - val_accuracy: 0.9876
Epoch 133/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0212 - accuracy: 0.9937 - val_loss: 0.0561 - val_accuracy: 0.9882
Epoch 134/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9945 - val_loss: 0.0567 - val_accuracy: 0.9883
Epoch 135/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0199 - accuracy: 0.9942 - val_loss: 0.0571 - val_accuracy: 0.9891
Epoch 136/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0178 - accuracy: 0.9943 - val_loss: 0.0548 - val_accuracy: 0.9896
Epoch 137/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9945 - val_loss: 0.0593 - val_accuracy: 0.9891
Epoch 138/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0185 - accuracy: 0.9944 - val_loss: 0.0660 - val_accuracy: 0.9878
Epoch 139/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 0.0613 - val_accuracy: 0.9887
Epoch 140/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0164 - accuracy: 0.9952 - val_loss: 0.0629 - val_accuracy: 0.9882
Epoch 141/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0183 - accuracy: 0.9946 - val_loss: 0.0587 - val_accuracy: 0.9883
Epoch 142/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0181 - accuracy: 0.9949 - val_loss: 0.0631 - val_accuracy: 0.9875
Epoch 143/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0207 - accuracy: 0.9941 - val_loss: 0.0588 - val_accuracy: 0.9888
Epoch 144/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0174 - accuracy: 0.9952 - val_loss: 0.0634 - val_accuracy: 0.9876
Epoch 145/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0176 - accuracy: 0.9947 - val_loss: 0.0629 - val_accuracy: 0.9884
Epoch 146/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0193 - accuracy: 0.9945 - val_loss: 0.0596 - val_accuracy: 0.9882
Epoch 147/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0178 - accuracy: 0.9944 - val_loss: 0.0598 - val_accuracy: 0.9891
Epoch 148/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0177 - accuracy: 0.9945 - val_loss: 0.0626 - val_accuracy: 0.9882
Epoch 149/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0185 - accuracy: 0.9947 - val_loss: 0.0589 - val_accuracy: 0.9877
Epoch 150/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0179 - accuracy: 0.9947 - val_loss: 0.0621 - val_accuracy: 0.9872
Epoch 151/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0170 - accuracy: 0.9949 - val_loss: 0.0675 - val_accuracy: 0.9880
Epoch 152/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0170 - accuracy: 0.9952 - val_loss: 0.0619 - val_accuracy: 0.9884
Epoch 153/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0174 - accuracy: 0.9950 - val_loss: 0.0613 - val_accuracy: 0.9887
Epoch 154/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0179 - accuracy: 0.9949 - val_loss: 0.0667 - val_accuracy: 0.9871
Epoch 155/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0171 - accuracy: 0.9950 - val_loss: 0.0616 - val_accuracy: 0.9884
Epoch 156/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0158 - accuracy: 0.9953 - val_loss: 0.0638 - val_accuracy: 0.9884
Epoch 157/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0191 - accuracy: 0.9944 - val_loss: 0.0623 - val_accuracy: 0.9887
Epoch 158/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0172 - accuracy: 0.9948 - val_loss: 0.0641 - val_accuracy: 0.9888
Epoch 159/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0164 - accuracy: 0.9952 - val_loss: 0.0657 - val_accuracy: 0.9885
Epoch 160/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0161 - accuracy: 0.9949 - val_loss: 0.0636 - val_accuracy: 0.9885
Epoch 161/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0201 - accuracy: 0.9941 - val_loss: 0.0618 - val_accuracy: 0.9878
Epoch 162/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0161 - accuracy: 0.9952 - val_loss: 0.0635 - val_accuracy: 0.9893
Epoch 163/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0169 - accuracy: 0.9951 - val_loss: 0.0635 - val_accuracy: 0.9870
Epoch 164/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0172 - accuracy: 0.9948 - val_loss: 0.0581 - val_accuracy: 0.9889
Epoch 165/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0182 - accuracy: 0.9948 - val_loss: 0.0589 - val_accuracy: 0.9879
Epoch 166/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - accuracy: 0.9954 - val_loss: 0.0598 - val_accuracy: 0.9889
Epoch 167/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0165 - accuracy: 0.9952 - val_loss: 0.0649 - val_accuracy: 0.9878
Epoch 168/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0180 - accuracy: 0.9948 - val_loss: 0.0585 - val_accuracy: 0.9878
Epoch 169/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0171 - accuracy: 0.9949 - val_loss: 0.0589 - val_accuracy: 0.9880
Epoch 170/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0156 - accuracy: 0.9955 - val_loss: 0.0602 - val_accuracy: 0.9879
Epoch 171/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0156 - accuracy: 0.9956 - val_loss: 0.0586 - val_accuracy: 0.9886
Epoch 172/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - accuracy: 0.9951 - val_loss: 0.0577 - val_accuracy: 0.9888
Epoch 173/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0190 - accuracy: 0.9946 - val_loss: 0.0551 - val_accuracy: 0.9896
Epoch 174/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9954 - val_loss: 0.0565 - val_accuracy: 0.9894
Epoch 175/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9952 - val_loss: 0.0600 - val_accuracy: 0.9880
Epoch 176/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.0562 - val_accuracy: 0.9892
Epoch 177/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0152 - accuracy: 0.9955 - val_loss: 0.0668 - val_accuracy: 0.9881
Epoch 178/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0171 - accuracy: 0.9948 - val_loss: 0.0642 - val_accuracy: 0.9870
Epoch 179/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0164 - accuracy: 0.9951 - val_loss: 0.0617 - val_accuracy: 0.9893
Epoch 180/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0162 - accuracy: 0.9954 - val_loss: 0.0610 - val_accuracy: 0.9892
Epoch 181/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0140 - accuracy: 0.9959 - val_loss: 0.0624 - val_accuracy: 0.9881
Epoch 182/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0167 - accuracy: 0.9951 - val_loss: 0.0650 - val_accuracy: 0.9874
Epoch 183/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0167 - accuracy: 0.9951 - val_loss: 0.0575 - val_accuracy: 0.9890
Epoch 184/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.0606 - val_accuracy: 0.9879
Epoch 185/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0175 - accuracy: 0.9951 - val_loss: 0.0617 - val_accuracy: 0.9886
Epoch 186/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9958 - val_loss: 0.0636 - val_accuracy: 0.9891
Epoch 187/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9962 - val_loss: 0.0676 - val_accuracy: 0.9883
Epoch 188/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9953 - val_loss: 0.0685 - val_accuracy: 0.9875
Epoch 189/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9954 - val_loss: 0.0696 - val_accuracy: 0.9877
Epoch 190/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9954 - val_loss: 0.0652 - val_accuracy: 0.9889
Epoch 191/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - accuracy: 0.9954 - val_loss: 0.0624 - val_accuracy: 0.9891
Epoch 192/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0166 - accuracy: 0.9952 - val_loss: 0.0661 - val_accuracy: 0.9886
Epoch 193/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0173 - accuracy: 0.9949 - val_loss: 0.0680 - val_accuracy: 0.9882
Epoch 194/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9956 - val_loss: 0.0639 - val_accuracy: 0.9889
Epoch 195/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0158 - accuracy: 0.9953 - val_loss: 0.0630 - val_accuracy: 0.9874
Epoch 196/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0175 - accuracy: 0.9949 - val_loss: 0.0599 - val_accuracy: 0.9884
Epoch 197/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0160 - accuracy: 0.9958 - val_loss: 0.0624 - val_accuracy: 0.9883
Epoch 198/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0149 - accuracy: 0.9955 - val_loss: 0.0669 - val_accuracy: 0.9879
Epoch 199/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0147 - accuracy: 0.9957 - val_loss: 0.0632 - val_accuracy: 0.9884
Epoch 200/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0168 - accuracy: 0.9951 - val_loss: 0.0599 - val_accuracy: 0.9880
Epoch 201/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9955 - val_loss: 0.0642 - val_accuracy: 0.9891
Epoch 202/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0168 - accuracy: 0.9954 - val_loss: 0.0592 - val_accuracy: 0.9892
Epoch 203/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0187 - accuracy: 0.9947 - val_loss: 0.0626 - val_accuracy: 0.9879
Epoch 204/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0165 - accuracy: 0.9956 - val_loss: 0.0629 - val_accuracy: 0.9876
Epoch 205/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0148 - accuracy: 0.9956 - val_loss: 0.0643 - val_accuracy: 0.9891
Epoch 206/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0157 - accuracy: 0.9955 - val_loss: 0.0628 - val_accuracy: 0.9880
Epoch 207/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9956 - val_loss: 0.0603 - val_accuracy: 0.9882
Epoch 208/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0177 - accuracy: 0.9949 - val_loss: 0.0616 - val_accuracy: 0.9889
Epoch 209/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0175 - accuracy: 0.9951 - val_loss: 0.0625 - val_accuracy: 0.9892
Epoch 210/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0165 - accuracy: 0.9952 - val_loss: 0.0596 - val_accuracy: 0.9889
Epoch 211/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0156 - accuracy: 0.9954 - val_loss: 0.0618 - val_accuracy: 0.9888
Epoch 212/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9958 - val_loss: 0.0608 - val_accuracy: 0.9889
Epoch 213/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0158 - accuracy: 0.9955 - val_loss: 0.0623 - val_accuracy: 0.9894
Epoch 214/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9961 - val_loss: 0.0604 - val_accuracy: 0.9900
Epoch 215/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0645 - val_accuracy: 0.9888
Epoch 216/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0158 - accuracy: 0.9955 - val_loss: 0.0696 - val_accuracy: 0.9877
Epoch 217/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9959 - val_loss: 0.0645 - val_accuracy: 0.9888
Epoch 218/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9956 - val_loss: 0.0761 - val_accuracy: 0.9881
Epoch 219/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0165 - accuracy: 0.9954 - val_loss: 0.0664 - val_accuracy: 0.9885
Epoch 220/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0164 - accuracy: 0.9951 - val_loss: 0.0669 - val_accuracy: 0.9884
Epoch 221/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0668 - val_accuracy: 0.9886
Epoch 222/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0141 - accuracy: 0.9961 - val_loss: 0.0709 - val_accuracy: 0.9890
Epoch 223/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9959 - val_loss: 0.0690 - val_accuracy: 0.9890
Epoch 224/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0157 - accuracy: 0.9955 - val_loss: 0.0700 - val_accuracy: 0.9883
Epoch 225/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0640 - val_accuracy: 0.9886
Epoch 226/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0153 - accuracy: 0.9953 - val_loss: 0.0650 - val_accuracy: 0.9889
Epoch 227/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9958 - val_loss: 0.0682 - val_accuracy: 0.9888
Epoch 228/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0167 - accuracy: 0.9953 - val_loss: 0.0639 - val_accuracy: 0.9884
Epoch 229/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0160 - accuracy: 0.9956 - val_loss: 0.0621 - val_accuracy: 0.9886
Epoch 230/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9960 - val_loss: 0.0678 - val_accuracy: 0.9882
Epoch 231/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9954 - val_loss: 0.0667 - val_accuracy: 0.9879
Epoch 232/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 0.0630 - val_accuracy: 0.9889
Epoch 233/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0153 - accuracy: 0.9959 - val_loss: 0.0629 - val_accuracy: 0.9884
Epoch 234/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9958 - val_loss: 0.0689 - val_accuracy: 0.9881
Epoch 235/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0159 - accuracy: 0.9954 - val_loss: 0.0628 - val_accuracy: 0.9886
Epoch 236/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0170 - accuracy: 0.9951 - val_loss: 0.0628 - val_accuracy: 0.9885
Epoch 237/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9961 - val_loss: 0.0689 - val_accuracy: 0.9887
Epoch 238/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 0.0626 - val_accuracy: 0.9899
Epoch 239/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.0639 - val_accuracy: 0.9897
Epoch 240/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9957 - val_loss: 0.0650 - val_accuracy: 0.9886
Epoch 241/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.0602 - val_accuracy: 0.9896
Epoch 242/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9961 - val_loss: 0.0624 - val_accuracy: 0.9895
Epoch 243/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9959 - val_loss: 0.0647 - val_accuracy: 0.9892
Epoch 244/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9955 - val_loss: 0.0605 - val_accuracy: 0.9896
Epoch 245/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.0635 - val_accuracy: 0.9893
Epoch 246/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9961 - val_loss: 0.0733 - val_accuracy: 0.9886
Epoch 247/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0158 - accuracy: 0.9956 - val_loss: 0.0661 - val_accuracy: 0.9885
Epoch 248/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.0682 - val_accuracy: 0.9892
Epoch 249/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9960 - val_loss: 0.0642 - val_accuracy: 0.9894
Epoch 250/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.0650 - val_accuracy: 0.9897
Epoch 251/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 0.0641 - val_accuracy: 0.9892
Epoch 252/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9962 - val_loss: 0.0606 - val_accuracy: 0.9896
Epoch 253/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 0.0662 - val_accuracy: 0.9886
Epoch 254/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9960 - val_loss: 0.0635 - val_accuracy: 0.9889
Epoch 255/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9961 - val_loss: 0.0605 - val_accuracy: 0.9893
Epoch 256/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9956 - val_loss: 0.0646 - val_accuracy: 0.9888
Epoch 257/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 0.0643 - val_accuracy: 0.9887
Epoch 258/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9962 - val_loss: 0.0563 - val_accuracy: 0.9898
Epoch 259/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9958 - val_loss: 0.0664 - val_accuracy: 0.9892
Epoch 260/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9963 - val_loss: 0.0626 - val_accuracy: 0.9894
Epoch 261/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0162 - accuracy: 0.9954 - val_loss: 0.0626 - val_accuracy: 0.9892
Epoch 262/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9958 - val_loss: 0.0654 - val_accuracy: 0.9887
Epoch 263/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9955 - val_loss: 0.0627 - val_accuracy: 0.9883
Epoch 264/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9964 - val_loss: 0.0687 - val_accuracy: 0.9893
Epoch 265/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0174 - accuracy: 0.9953 - val_loss: 0.0610 - val_accuracy: 0.9887
Epoch 266/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9961 - val_loss: 0.0668 - val_accuracy: 0.9882
Epoch 267/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9958 - val_loss: 0.0615 - val_accuracy: 0.9890
Epoch 268/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9957 - val_loss: 0.0664 - val_accuracy: 0.9890
Epoch 269/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9960 - val_loss: 0.0672 - val_accuracy: 0.9883
Epoch 270/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9957 - val_loss: 0.0636 - val_accuracy: 0.9888
Epoch 271/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9965 - val_loss: 0.0684 - val_accuracy: 0.9883
Epoch 272/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 0.0680 - val_accuracy: 0.9882
Epoch 273/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9961 - val_loss: 0.0726 - val_accuracy: 0.9881
Epoch 274/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9955 - val_loss: 0.0640 - val_accuracy: 0.9881
Epoch 275/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9965 - val_loss: 0.0699 - val_accuracy: 0.9884
Epoch 276/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 0.0695 - val_accuracy: 0.9888
Epoch 277/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9961 - val_loss: 0.0729 - val_accuracy: 0.9883
Epoch 278/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0160 - accuracy: 0.9957 - val_loss: 0.0618 - val_accuracy: 0.9884
Epoch 279/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0141 - accuracy: 0.9958 - val_loss: 0.0659 - val_accuracy: 0.9887
Epoch 280/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9958 - val_loss: 0.0704 - val_accuracy: 0.9881
Epoch 281/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9960 - val_loss: 0.0671 - val_accuracy: 0.9899
Epoch 282/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.0683 - val_accuracy: 0.9883
Epoch 283/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9959 - val_loss: 0.0675 - val_accuracy: 0.9881
Epoch 284/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9964 - val_loss: 0.0689 - val_accuracy: 0.9891
Epoch 285/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0149 - accuracy: 0.9959 - val_loss: 0.0651 - val_accuracy: 0.9877
Epoch 286/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9959 - val_loss: 0.0615 - val_accuracy: 0.9889
Epoch 287/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9958 - val_loss: 0.0619 - val_accuracy: 0.9886
Epoch 288/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0137 - accuracy: 0.9964 - val_loss: 0.0625 - val_accuracy: 0.9893
Epoch 289/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0143 - accuracy: 0.9957 - val_loss: 0.0705 - val_accuracy: 0.9887
Epoch 290/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0139 - accuracy: 0.9963 - val_loss: 0.0714 - val_accuracy: 0.9889
Epoch 291/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9959 - val_loss: 0.0725 - val_accuracy: 0.9896
Epoch 292/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0157 - accuracy: 0.9956 - val_loss: 0.0653 - val_accuracy: 0.9886
Epoch 293/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9961 - val_loss: 0.0692 - val_accuracy: 0.9886
Epoch 294/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 0.0682 - val_accuracy: 0.9891
Epoch 295/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0137 - accuracy: 0.9961 - val_loss: 0.0625 - val_accuracy: 0.9887
Epoch 296/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9962 - val_loss: 0.0645 - val_accuracy: 0.9895
Epoch 297/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9958 - val_loss: 0.0650 - val_accuracy: 0.9879
Epoch 298/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9962 - val_loss: 0.0690 - val_accuracy: 0.9882
Epoch 299/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9965 - val_loss: 0.0665 - val_accuracy: 0.9885
Epoch 300/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0131 - accuracy: 0.9963 - val_loss: 0.0753 - val_accuracy: 0.9885
Epoch 301/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9961 - val_loss: 0.0634 - val_accuracy: 0.9900
Epoch 302/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9957 - val_loss: 0.0617 - val_accuracy: 0.9895
Epoch 303/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9965 - val_loss: 0.0629 - val_accuracy: 0.9883
Epoch 304/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9960 - val_loss: 0.0601 - val_accuracy: 0.9891
Epoch 305/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9962 - val_loss: 0.0629 - val_accuracy: 0.9880
Epoch 306/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0135 - accuracy: 0.9962 - val_loss: 0.0675 - val_accuracy: 0.9887
Epoch 307/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9965 - val_loss: 0.0755 - val_accuracy: 0.9884
Epoch 308/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9965 - val_loss: 0.0678 - val_accuracy: 0.9889
Epoch 309/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9968 - val_loss: 0.0672 - val_accuracy: 0.9887
Epoch 310/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9961 - val_loss: 0.0647 - val_accuracy: 0.9886
Epoch 311/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.0663 - val_accuracy: 0.9881
Epoch 312/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9961 - val_loss: 0.0668 - val_accuracy: 0.9881
Epoch 313/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 0.0646 - val_accuracy: 0.9884
Epoch 314/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 0.0718 - val_accuracy: 0.9880
Epoch 315/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0141 - accuracy: 0.9963 - val_loss: 0.0651 - val_accuracy: 0.9875
Epoch 316/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9956 - val_loss: 0.0713 - val_accuracy: 0.9875
Epoch 317/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9960 - val_loss: 0.0661 - val_accuracy: 0.9882
Epoch 318/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9963 - val_loss: 0.0714 - val_accuracy: 0.9874
Epoch 319/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9963 - val_loss: 0.0658 - val_accuracy: 0.9886
Epoch 320/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9965 - val_loss: 0.0689 - val_accuracy: 0.9890
Epoch 321/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9963 - val_loss: 0.0649 - val_accuracy: 0.9888
Epoch 322/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9960 - val_loss: 0.0646 - val_accuracy: 0.9892
Epoch 323/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9966 - val_loss: 0.0712 - val_accuracy: 0.9885
Epoch 324/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9963 - val_loss: 0.0691 - val_accuracy: 0.9882
Epoch 325/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9963 - val_loss: 0.0736 - val_accuracy: 0.9885
Epoch 326/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0130 - accuracy: 0.9964 - val_loss: 0.0710 - val_accuracy: 0.9896
Epoch 327/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9964 - val_loss: 0.0720 - val_accuracy: 0.9894
Epoch 328/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9965 - val_loss: 0.0627 - val_accuracy: 0.9891
Epoch 329/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9967 - val_loss: 0.0639 - val_accuracy: 0.9890
Epoch 330/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9966 - val_loss: 0.0667 - val_accuracy: 0.9886
Epoch 331/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9965 - val_loss: 0.0615 - val_accuracy: 0.9893
Epoch 332/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9962 - val_loss: 0.0635 - val_accuracy: 0.9890
Epoch 333/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0151 - accuracy: 0.9957 - val_loss: 0.0690 - val_accuracy: 0.9884
Epoch 334/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9960 - val_loss: 0.0629 - val_accuracy: 0.9888
Epoch 335/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9961 - val_loss: 0.0664 - val_accuracy: 0.9885
Epoch 336/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9965 - val_loss: 0.0704 - val_accuracy: 0.9885
Epoch 337/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9966 - val_loss: 0.0620 - val_accuracy: 0.9894
Epoch 338/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9960 - val_loss: 0.0663 - val_accuracy: 0.9894
Epoch 339/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9965 - val_loss: 0.0693 - val_accuracy: 0.9893
Epoch 340/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9966 - val_loss: 0.0643 - val_accuracy: 0.9881
Epoch 341/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9959 - val_loss: 0.0678 - val_accuracy: 0.9885
Epoch 342/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9965 - val_loss: 0.0664 - val_accuracy: 0.9896
Epoch 343/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9966 - val_loss: 0.0699 - val_accuracy: 0.9885
Epoch 344/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9963 - val_loss: 0.0695 - val_accuracy: 0.9882
Epoch 345/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9964 - val_loss: 0.0703 - val_accuracy: 0.9890
Epoch 346/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0155 - accuracy: 0.9962 - val_loss: 0.0695 - val_accuracy: 0.9884
Epoch 347/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0162 - accuracy: 0.9961 - val_loss: 0.0603 - val_accuracy: 0.9897
Epoch 348/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0131 - accuracy: 0.9962 - val_loss: 0.0637 - val_accuracy: 0.9893
Epoch 349/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0131 - accuracy: 0.9961 - val_loss: 0.0654 - val_accuracy: 0.9887
Epoch 350/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0151 - accuracy: 0.9959 - val_loss: 0.0602 - val_accuracy: 0.9891
Epoch 351/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0126 - accuracy: 0.9963 - val_loss: 0.0629 - val_accuracy: 0.9899
Epoch 352/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9960 - val_loss: 0.0658 - val_accuracy: 0.9894
Epoch 353/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9965 - val_loss: 0.0703 - val_accuracy: 0.9890
Epoch 354/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9968 - val_loss: 0.0717 - val_accuracy: 0.9889
Epoch 355/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.0687 - val_accuracy: 0.9877
Epoch 356/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9966 - val_loss: 0.0713 - val_accuracy: 0.9893
Epoch 357/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9959 - val_loss: 0.0675 - val_accuracy: 0.9887
Epoch 358/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9960 - val_loss: 0.0610 - val_accuracy: 0.9887
Epoch 359/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9964 - val_loss: 0.0669 - val_accuracy: 0.9891
Epoch 360/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9971 - val_loss: 0.0693 - val_accuracy: 0.9875
Epoch 361/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9966 - val_loss: 0.0675 - val_accuracy: 0.9891
Epoch 362/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9962 - val_loss: 0.0650 - val_accuracy: 0.9890
Epoch 363/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9965 - val_loss: 0.0623 - val_accuracy: 0.9892
Epoch 364/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9963 - val_loss: 0.0674 - val_accuracy: 0.9890
Epoch 365/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9964 - val_loss: 0.0708 - val_accuracy: 0.9893
Epoch 366/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.0660 - val_accuracy: 0.9889
Epoch 367/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 0.0705 - val_accuracy: 0.9882
Epoch 368/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9965 - val_loss: 0.0706 - val_accuracy: 0.9889
Epoch 369/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9963 - val_loss: 0.0685 - val_accuracy: 0.9882
Epoch 370/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.0752 - val_accuracy: 0.9887
Epoch 371/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9969 - val_loss: 0.0760 - val_accuracy: 0.9882
Epoch 372/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9967 - val_loss: 0.0675 - val_accuracy: 0.9887
Epoch 373/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9966 - val_loss: 0.0661 - val_accuracy: 0.9886
Epoch 374/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9965 - val_loss: 0.0633 - val_accuracy: 0.9892
Epoch 375/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.0716 - val_accuracy: 0.9889
Epoch 376/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9960 - val_loss: 0.0742 - val_accuracy: 0.9879
Epoch 377/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9965 - val_loss: 0.0725 - val_accuracy: 0.9889
Epoch 378/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9962 - val_loss: 0.0697 - val_accuracy: 0.9887
Epoch 379/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9964 - val_loss: 0.0703 - val_accuracy: 0.9888
Epoch 380/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0123 - accuracy: 0.9965 - val_loss: 0.0668 - val_accuracy: 0.9886
Epoch 381/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9962 - val_loss: 0.0641 - val_accuracy: 0.9883
Epoch 382/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.0712 - val_accuracy: 0.9885
Epoch 383/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9960 - val_loss: 0.0726 - val_accuracy: 0.9883
Epoch 384/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0139 - accuracy: 0.9961 - val_loss: 0.0692 - val_accuracy: 0.9880
Epoch 385/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9964 - val_loss: 0.0640 - val_accuracy: 0.9885
Epoch 386/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0128 - accuracy: 0.9967 - val_loss: 0.0643 - val_accuracy: 0.9891
Epoch 387/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9967 - val_loss: 0.0628 - val_accuracy: 0.9899
Epoch 388/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0121 - accuracy: 0.9970 - val_loss: 0.0629 - val_accuracy: 0.9893
Epoch 389/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 0.0663 - val_accuracy: 0.9888
Epoch 390/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.0673 - val_accuracy: 0.9892
Epoch 391/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9966 - val_loss: 0.0680 - val_accuracy: 0.9899
Epoch 392/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9963 - val_loss: 0.0680 - val_accuracy: 0.9892
Epoch 393/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9966 - val_loss: 0.0686 - val_accuracy: 0.9894
Epoch 394/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9962 - val_loss: 0.0707 - val_accuracy: 0.9885
Epoch 395/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9970 - val_loss: 0.0737 - val_accuracy: 0.9883
Epoch 396/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0109 - accuracy: 0.9969 - val_loss: 0.0772 - val_accuracy: 0.9891
Epoch 397/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9969 - val_loss: 0.0709 - val_accuracy: 0.9883
Epoch 398/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0145 - accuracy: 0.9962 - val_loss: 0.0706 - val_accuracy: 0.9890
Epoch 399/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9963 - val_loss: 0.0724 - val_accuracy: 0.9885
Epoch 400/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9964 - val_loss: 0.0673 - val_accuracy: 0.9885
Epoch 401/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.0686 - val_accuracy: 0.9890
Epoch 402/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9967 - val_loss: 0.0697 - val_accuracy: 0.9893
Epoch 403/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.0649 - val_accuracy: 0.9897
Epoch 404/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0128 - accuracy: 0.9963 - val_loss: 0.0743 - val_accuracy: 0.9892
Epoch 405/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9962 - val_loss: 0.0630 - val_accuracy: 0.9893
Epoch 406/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0112 - accuracy: 0.9967 - val_loss: 0.0716 - val_accuracy: 0.9895
Epoch 407/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9965 - val_loss: 0.0675 - val_accuracy: 0.9893
Epoch 408/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9971 - val_loss: 0.0637 - val_accuracy: 0.9895
Epoch 409/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9967 - val_loss: 0.0692 - val_accuracy: 0.9888
Epoch 410/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9965 - val_loss: 0.0676 - val_accuracy: 0.9893
Epoch 411/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9967 - val_loss: 0.0673 - val_accuracy: 0.9895
Epoch 412/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 0.0687 - val_accuracy: 0.9899
Epoch 413/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9964 - val_loss: 0.0654 - val_accuracy: 0.9898
Epoch 414/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9966 - val_loss: 0.0702 - val_accuracy: 0.9889
Epoch 415/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9967 - val_loss: 0.0707 - val_accuracy: 0.9888
Epoch 416/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9966 - val_loss: 0.0665 - val_accuracy: 0.9900
Epoch 417/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9969 - val_loss: 0.0654 - val_accuracy: 0.9894
Epoch 418/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9965 - val_loss: 0.0655 - val_accuracy: 0.9890
Epoch 419/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.0663 - val_accuracy: 0.9896
Epoch 420/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9966 - val_loss: 0.0674 - val_accuracy: 0.9894
Epoch 421/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9963 - val_loss: 0.0622 - val_accuracy: 0.9898
Epoch 422/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9966 - val_loss: 0.0638 - val_accuracy: 0.9902
Epoch 423/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9967 - val_loss: 0.0644 - val_accuracy: 0.9905
Epoch 424/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9969 - val_loss: 0.0673 - val_accuracy: 0.9894
Epoch 425/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9967 - val_loss: 0.0692 - val_accuracy: 0.9895
Epoch 426/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9969 - val_loss: 0.0673 - val_accuracy: 0.9897
Epoch 427/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9965 - val_loss: 0.0676 - val_accuracy: 0.9881
Epoch 428/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0124 - accuracy: 0.9968 - val_loss: 0.0673 - val_accuracy: 0.9891
Epoch 429/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9965 - val_loss: 0.0677 - val_accuracy: 0.9897
Epoch 430/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9965 - val_loss: 0.0698 - val_accuracy: 0.9894
Epoch 431/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9962 - val_loss: 0.0700 - val_accuracy: 0.9886
Epoch 432/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9963 - val_loss: 0.0697 - val_accuracy: 0.9894
Epoch 433/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9965 - val_loss: 0.0639 - val_accuracy: 0.9892
Epoch 434/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9967 - val_loss: 0.0698 - val_accuracy: 0.9885
Epoch 435/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0124 - accuracy: 0.9969 - val_loss: 0.0664 - val_accuracy: 0.9888
Epoch 436/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 0.0638 - val_accuracy: 0.9895
Epoch 437/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9963 - val_loss: 0.0651 - val_accuracy: 0.9888
Epoch 438/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0121 - accuracy: 0.9967 - val_loss: 0.0688 - val_accuracy: 0.9889
Epoch 439/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9965 - val_loss: 0.0670 - val_accuracy: 0.9892
Epoch 440/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9970 - val_loss: 0.0676 - val_accuracy: 0.9889
Epoch 441/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9964 - val_loss: 0.0686 - val_accuracy: 0.9884
Epoch 442/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9966 - val_loss: 0.0722 - val_accuracy: 0.9887
Epoch 443/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9968 - val_loss: 0.0719 - val_accuracy: 0.9884
Epoch 444/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9963 - val_loss: 0.0696 - val_accuracy: 0.9893
Epoch 445/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 0.0705 - val_accuracy: 0.9894
Epoch 446/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9965 - val_loss: 0.0693 - val_accuracy: 0.9890
Epoch 447/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9964 - val_loss: 0.0774 - val_accuracy: 0.9889
Epoch 448/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9963 - val_loss: 0.0703 - val_accuracy: 0.9884
Epoch 449/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9963 - val_loss: 0.0689 - val_accuracy: 0.9900
Epoch 450/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9967 - val_loss: 0.0690 - val_accuracy: 0.9893
Epoch 451/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9971 - val_loss: 0.0677 - val_accuracy: 0.9894
Epoch 452/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9967 - val_loss: 0.0679 - val_accuracy: 0.9897
Epoch 453/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 0.0706 - val_accuracy: 0.9892
Epoch 454/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9966 - val_loss: 0.0767 - val_accuracy: 0.9892
Epoch 455/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0139 - accuracy: 0.9962 - val_loss: 0.0712 - val_accuracy: 0.9879
Epoch 456/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9963 - val_loss: 0.0713 - val_accuracy: 0.9893
Epoch 457/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9966 - val_loss: 0.0709 - val_accuracy: 0.9889
Epoch 458/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9969 - val_loss: 0.0754 - val_accuracy: 0.9880
Epoch 459/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9966 - val_loss: 0.0764 - val_accuracy: 0.9883
Epoch 460/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9970 - val_loss: 0.0746 - val_accuracy: 0.9892
Epoch 461/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9968 - val_loss: 0.0752 - val_accuracy: 0.9885
Epoch 462/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 0.0726 - val_accuracy: 0.9891
Epoch 463/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9969 - val_loss: 0.0775 - val_accuracy: 0.9896
Epoch 464/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9965 - val_loss: 0.0748 - val_accuracy: 0.9885
Epoch 465/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9966 - val_loss: 0.0763 - val_accuracy: 0.9883
Epoch 466/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9965 - val_loss: 0.0796 - val_accuracy: 0.9882
Epoch 467/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9967 - val_loss: 0.0743 - val_accuracy: 0.9888
Epoch 468/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9970 - val_loss: 0.0760 - val_accuracy: 0.9891
Epoch 469/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9968 - val_loss: 0.0768 - val_accuracy: 0.9890
Epoch 470/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9965 - val_loss: 0.0725 - val_accuracy: 0.9886
Epoch 471/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9966 - val_loss: 0.0720 - val_accuracy: 0.9888
Epoch 472/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0110 - accuracy: 0.9972 - val_loss: 0.0708 - val_accuracy: 0.9885
Epoch 473/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9968 - val_loss: 0.0699 - val_accuracy: 0.9886
Epoch 474/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9966 - val_loss: 0.0765 - val_accuracy: 0.9883
Epoch 475/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9968 - val_loss: 0.0735 - val_accuracy: 0.9879
Epoch 476/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 0.0666 - val_accuracy: 0.9888
Epoch 477/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9969 - val_loss: 0.0683 - val_accuracy: 0.9892
Epoch 478/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0112 - accuracy: 0.9969 - val_loss: 0.0718 - val_accuracy: 0.9882
Epoch 479/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 0.0705 - val_accuracy: 0.9886
Epoch 480/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9969 - val_loss: 0.0671 - val_accuracy: 0.9898
Epoch 481/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9964 - val_loss: 0.0778 - val_accuracy: 0.9883
Epoch 482/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9967 - val_loss: 0.0781 - val_accuracy: 0.9889
Epoch 483/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9969 - val_loss: 0.0768 - val_accuracy: 0.9882
Epoch 484/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9967 - val_loss: 0.0757 - val_accuracy: 0.9886
Epoch 485/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9962 - val_loss: 0.0683 - val_accuracy: 0.9882
Epoch 486/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.0685 - val_accuracy: 0.9883
Epoch 487/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9966 - val_loss: 0.0697 - val_accuracy: 0.9884
Epoch 488/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9963 - val_loss: 0.0659 - val_accuracy: 0.9887
Epoch 489/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0112 - accuracy: 0.9972 - val_loss: 0.0682 - val_accuracy: 0.9892
Epoch 490/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9966 - val_loss: 0.0691 - val_accuracy: 0.9885
Epoch 491/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9966 - val_loss: 0.0658 - val_accuracy: 0.9889
Epoch 492/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0128 - accuracy: 0.9967 - val_loss: 0.0734 - val_accuracy: 0.9880
Epoch 493/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 0.0746 - val_accuracy: 0.9883
Epoch 494/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9968 - val_loss: 0.0725 - val_accuracy: 0.9886
Epoch 495/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0132 - accuracy: 0.9968 - val_loss: 0.0679 - val_accuracy: 0.9892
Epoch 496/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9969 - val_loss: 0.0753 - val_accuracy: 0.9883
Epoch 497/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9968 - val_loss: 0.0785 - val_accuracy: 0.9885
Epoch 498/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0099 - accuracy: 0.9975 - val_loss: 0.0752 - val_accuracy: 0.9887
Epoch 499/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0121 - accuracy: 0.9963 - val_loss: 0.0722 - val_accuracy: 0.9890
Epoch 500/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0129 - accuracy: 0.9967 - val_loss: 0.0689 - val_accuracy: 0.9895

Activation function: ReLU; initialization: Kaiming He’s initializer; no dropout

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.HeNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 784)               0         
_________________________________________________________________
dense (Dense)                (None, 1024)              803840    
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_2 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_3 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_4 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dense_5 (Dense)              (None, 10)                10250     
=================================================================
Total params: 5,012,490
Trainable params: 5,012,490
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam()
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
history1 = model.fit(X_train, y_train, batch_size=128, epochs=500, validation_data=(X_test, y_test))
Epoch 1/500
469/469 [==============================] - 2s 5ms/step - loss: 0.2249 - accuracy: 0.9318 - val_loss: 0.1190 - val_accuracy: 0.9646
Epoch 2/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0928 - accuracy: 0.9729 - val_loss: 0.1528 - val_accuracy: 0.9613
Epoch 3/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0674 - accuracy: 0.9799 - val_loss: 0.0949 - val_accuracy: 0.9724
Epoch 4/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0508 - accuracy: 0.9849 - val_loss: 0.0775 - val_accuracy: 0.9781
Epoch 5/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0445 - accuracy: 0.9864 - val_loss: 0.0799 - val_accuracy: 0.9808
Epoch 6/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0368 - accuracy: 0.9893 - val_loss: 0.0847 - val_accuracy: 0.9801
Epoch 7/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0339 - accuracy: 0.9903 - val_loss: 0.0791 - val_accuracy: 0.9817
Epoch 8/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0321 - accuracy: 0.9909 - val_loss: 0.0875 - val_accuracy: 0.9787
Epoch 9/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0275 - accuracy: 0.9923 - val_loss: 0.0834 - val_accuracy: 0.9813
Epoch 10/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0269 - accuracy: 0.9924 - val_loss: 0.0858 - val_accuracy: 0.9794
Epoch 11/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0298 - accuracy: 0.9923 - val_loss: 0.0892 - val_accuracy: 0.9793
Epoch 12/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0203 - accuracy: 0.9950 - val_loss: 0.1187 - val_accuracy: 0.9794
Epoch 13/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0233 - accuracy: 0.9940 - val_loss: 0.0849 - val_accuracy: 0.9837
Epoch 14/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0222 - accuracy: 0.9946 - val_loss: 0.0934 - val_accuracy: 0.9841
Epoch 15/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0205 - accuracy: 0.9948 - val_loss: 0.1004 - val_accuracy: 0.9790
Epoch 16/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0231 - accuracy: 0.9943 - val_loss: 0.1270 - val_accuracy: 0.9799
Epoch 17/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0188 - accuracy: 0.9953 - val_loss: 0.1141 - val_accuracy: 0.9820
Epoch 18/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9971 - val_loss: 0.1153 - val_accuracy: 0.9827
Epoch 19/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0164 - accuracy: 0.9962 - val_loss: 0.1125 - val_accuracy: 0.9803
Epoch 20/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0188 - accuracy: 0.9957 - val_loss: 0.1235 - val_accuracy: 0.9816
Epoch 21/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0178 - accuracy: 0.9958 - val_loss: 0.0939 - val_accuracy: 0.9844
Epoch 22/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9962 - val_loss: 0.0941 - val_accuracy: 0.9846
Epoch 23/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0173 - accuracy: 0.9959 - val_loss: 0.1025 - val_accuracy: 0.9805
Epoch 24/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9969 - val_loss: 0.1467 - val_accuracy: 0.9783
Epoch 25/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0144 - accuracy: 0.9965 - val_loss: 0.1117 - val_accuracy: 0.9826
Epoch 26/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0134 - accuracy: 0.9971 - val_loss: 0.1213 - val_accuracy: 0.9828
Epoch 27/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0099 - accuracy: 0.9974 - val_loss: 0.1256 - val_accuracy: 0.9852
Epoch 28/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0112 - accuracy: 0.9972 - val_loss: 0.1439 - val_accuracy: 0.9838
Epoch 29/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9973 - val_loss: 0.1244 - val_accuracy: 0.9793
Epoch 30/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0122 - accuracy: 0.9976 - val_loss: 0.1576 - val_accuracy: 0.9805
Epoch 31/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9975 - val_loss: 0.1096 - val_accuracy: 0.9845
Epoch 32/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9974 - val_loss: 0.1108 - val_accuracy: 0.9846
Epoch 33/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0088 - accuracy: 0.9982 - val_loss: 0.1480 - val_accuracy: 0.9756
Epoch 34/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0166 - accuracy: 0.9973 - val_loss: 0.1542 - val_accuracy: 0.9796
Epoch 35/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9984 - val_loss: 0.1012 - val_accuracy: 0.9845
Epoch 36/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9979 - val_loss: 0.1098 - val_accuracy: 0.9831
Epoch 37/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9975 - val_loss: 0.1508 - val_accuracy: 0.9826
Epoch 38/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0131 - accuracy: 0.9972 - val_loss: 0.1388 - val_accuracy: 0.9833
Epoch 39/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9977 - val_loss: 0.1356 - val_accuracy: 0.9823
Epoch 40/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0061 - accuracy: 0.9986 - val_loss: 0.1383 - val_accuracy: 0.9823
Epoch 41/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0063 - accuracy: 0.9986 - val_loss: 0.1482 - val_accuracy: 0.9841
Epoch 42/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0104 - accuracy: 0.9977 - val_loss: 0.1621 - val_accuracy: 0.9829
Epoch 43/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0073 - accuracy: 0.9987 - val_loss: 0.1723 - val_accuracy: 0.9824
Epoch 44/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9981 - val_loss: 0.1467 - val_accuracy: 0.9832
Epoch 45/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0102 - accuracy: 0.9980 - val_loss: 0.1533 - val_accuracy: 0.9833
Epoch 46/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0063 - accuracy: 0.9985 - val_loss: 0.1853 - val_accuracy: 0.9837
Epoch 47/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0138 - accuracy: 0.9978 - val_loss: 0.1365 - val_accuracy: 0.9837
Epoch 48/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9983 - val_loss: 0.1824 - val_accuracy: 0.9810
Epoch 49/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0082 - accuracy: 0.9984 - val_loss: 0.1620 - val_accuracy: 0.9847
Epoch 50/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 0.1419 - val_accuracy: 0.9820
Epoch 51/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9980 - val_loss: 0.1397 - val_accuracy: 0.9831
Epoch 52/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0079 - accuracy: 0.9983 - val_loss: 0.1095 - val_accuracy: 0.9848
Epoch 53/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0087 - accuracy: 0.9987 - val_loss: 0.1132 - val_accuracy: 0.9824
Epoch 54/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0036 - accuracy: 0.9993 - val_loss: 0.1660 - val_accuracy: 0.9851
Epoch 55/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9983 - val_loss: 0.1549 - val_accuracy: 0.9838
Epoch 56/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0063 - accuracy: 0.9989 - val_loss: 0.1878 - val_accuracy: 0.9818
Epoch 57/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0127 - accuracy: 0.9980 - val_loss: 0.1686 - val_accuracy: 0.9823
Epoch 58/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9979 - val_loss: 0.1467 - val_accuracy: 0.9832
Epoch 59/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0085 - accuracy: 0.9982 - val_loss: 0.1497 - val_accuracy: 0.9839
Epoch 60/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0056 - accuracy: 0.9990 - val_loss: 0.1738 - val_accuracy: 0.9834
Epoch 61/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0154 - accuracy: 0.9980 - val_loss: 0.1658 - val_accuracy: 0.9815
Epoch 62/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0085 - accuracy: 0.9981 - val_loss: 0.1635 - val_accuracy: 0.9834
Epoch 63/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.1788 - val_accuracy: 0.9859
Epoch 64/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.2588 - val_accuracy: 0.9833
Epoch 65/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0096 - accuracy: 0.9985 - val_loss: 0.1957 - val_accuracy: 0.9849
Epoch 66/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0100 - accuracy: 0.9986 - val_loss: 0.1739 - val_accuracy: 0.9776
Epoch 67/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0070 - accuracy: 0.9983 - val_loss: 0.1896 - val_accuracy: 0.9829
Epoch 68/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0078 - accuracy: 0.9984 - val_loss: 0.1490 - val_accuracy: 0.9849
Epoch 69/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9981 - val_loss: 0.1578 - val_accuracy: 0.9831
Epoch 70/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0094 - accuracy: 0.9984 - val_loss: 0.1507 - val_accuracy: 0.9865
Epoch 71/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0053 - accuracy: 0.9991 - val_loss: 0.1428 - val_accuracy: 0.9859
Epoch 72/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0050 - accuracy: 0.9990 - val_loss: 0.1631 - val_accuracy: 0.9863
Epoch 73/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0089 - accuracy: 0.9988 - val_loss: 0.1880 - val_accuracy: 0.9814
Epoch 74/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0141 - accuracy: 0.9977 - val_loss: 0.1280 - val_accuracy: 0.9839
Epoch 75/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0013 - accuracy: 0.9998 - val_loss: 0.1745 - val_accuracy: 0.9871
Epoch 76/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.1707 - val_accuracy: 0.9867
Epoch 77/500
469/469 [==============================] - 2s 4ms/step - loss: 6.0835e-06 - accuracy: 1.0000 - val_loss: 0.1884 - val_accuracy: 0.9864
Epoch 78/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1267e-06 - accuracy: 1.0000 - val_loss: 0.1922 - val_accuracy: 0.9866
Epoch 79/500
469/469 [==============================] - 2s 4ms/step - loss: 6.9750e-07 - accuracy: 1.0000 - val_loss: 0.1960 - val_accuracy: 0.9866
Epoch 80/500
469/469 [==============================] - 2s 4ms/step - loss: 4.4371e-07 - accuracy: 1.0000 - val_loss: 0.2001 - val_accuracy: 0.9866
Epoch 81/500
469/469 [==============================] - 2s 4ms/step - loss: 2.8882e-07 - accuracy: 1.0000 - val_loss: 0.2040 - val_accuracy: 0.9867
Epoch 82/500
469/469 [==============================] - 2s 4ms/step - loss: 1.8003e-07 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9868
Epoch 83/500
469/469 [==============================] - 2s 4ms/step - loss: 9.0416e-08 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9868
Epoch 84/500
469/469 [==============================] - 2s 4ms/step - loss: 5.1665e-08 - accuracy: 1.0000 - val_loss: 0.2181 - val_accuracy: 0.9868
Epoch 85/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5470e-08 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.9868
Epoch 86/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5689e-08 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9868
Epoch 87/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9099e-08 - accuracy: 1.0000 - val_loss: 0.2286 - val_accuracy: 0.9869
Epoch 88/500
469/469 [==============================] - 2s 4ms/step - loss: 1.4506e-08 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9869
Epoch 89/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1176e-08 - accuracy: 1.0000 - val_loss: 0.2348 - val_accuracy: 0.9870
Epoch 90/500
469/469 [==============================] - 2s 4ms/step - loss: 8.5393e-09 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9870
Epoch 91/500
469/469 [==============================] - 2s 4ms/step - loss: 6.5863e-09 - accuracy: 1.0000 - val_loss: 0.2405 - val_accuracy: 0.9870
Epoch 92/500
469/469 [==============================] - 2s 4ms/step - loss: 5.1300e-09 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9871
Epoch 93/500
469/469 [==============================] - 2s 4ms/step - loss: 4.0392e-09 - accuracy: 1.0000 - val_loss: 0.2458 - val_accuracy: 0.9871
Epoch 94/500
469/469 [==============================] - 2s 4ms/step - loss: 3.2266e-09 - accuracy: 1.0000 - val_loss: 0.2483 - val_accuracy: 0.9871
Epoch 95/500
469/469 [==============================] - 2s 4ms/step - loss: 2.5988e-09 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9870
Epoch 96/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0742e-09 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9870
Epoch 97/500
469/469 [==============================] - 2s 4ms/step - loss: 1.7007e-09 - accuracy: 1.0000 - val_loss: 0.2551 - val_accuracy: 0.9870
Epoch 98/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3789e-09 - accuracy: 1.0000 - val_loss: 0.2574 - val_accuracy: 0.9871
Epoch 99/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1444e-09 - accuracy: 1.0000 - val_loss: 0.2594 - val_accuracy: 0.9871
Epoch 100/500
469/469 [==============================] - 2s 4ms/step - loss: 9.4374e-10 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9871
Epoch 101/500
469/469 [==============================] - 2s 4ms/step - loss: 7.7486e-10 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9871
Epoch 102/500
469/469 [==============================] - 2s 4ms/step - loss: 6.2784e-10 - accuracy: 1.0000 - val_loss: 0.2652 - val_accuracy: 0.9871
Epoch 103/500
469/469 [==============================] - 2s 4ms/step - loss: 5.2452e-10 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9871
Epoch 104/500
469/469 [==============================] - 2s 4ms/step - loss: 4.3313e-10 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9871
Epoch 105/500
469/469 [==============================] - 2s 4ms/step - loss: 3.6160e-10 - accuracy: 1.0000 - val_loss: 0.2705 - val_accuracy: 0.9871
Epoch 106/500
469/469 [==============================] - 2s 4ms/step - loss: 2.9405e-10 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9871
Epoch 107/500
469/469 [==============================] - 2s 4ms/step - loss: 2.4835e-10 - accuracy: 1.0000 - val_loss: 0.2738 - val_accuracy: 0.9871
Epoch 108/500
469/469 [==============================] - 2s 4ms/step - loss: 2.0663e-10 - accuracy: 1.0000 - val_loss: 0.2754 - val_accuracy: 0.9871
Epoch 109/500
469/469 [==============================] - 2s 4ms/step - loss: 1.6292e-10 - accuracy: 1.0000 - val_loss: 0.2770 - val_accuracy: 0.9871
Epoch 110/500
469/469 [==============================] - 2s 4ms/step - loss: 1.2914e-10 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9871
Epoch 111/500
469/469 [==============================] - 2s 4ms/step - loss: 1.1126e-10 - accuracy: 1.0000 - val_loss: 0.2800 - val_accuracy: 0.9871
Epoch 112/500
469/469 [==============================] - 2s 4ms/step - loss: 8.5433e-11 - accuracy: 1.0000 - val_loss: 0.2815 - val_accuracy: 0.9871
Epoch 113/500
469/469 [==============================] - 2s 4ms/step - loss: 7.3512e-11 - accuracy: 1.0000 - val_loss: 0.2829 - val_accuracy: 0.9871
Epoch 114/500
469/469 [==============================] - 2s 4ms/step - loss: 5.7618e-11 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9871
Epoch 115/500
469/469 [==============================] - 2s 4ms/step - loss: 4.7684e-11 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9871
Epoch 116/500
469/469 [==============================] - 2s 4ms/step - loss: 3.5763e-11 - accuracy: 1.0000 - val_loss: 0.2869 - val_accuracy: 0.9871
Epoch 117/500
469/469 [==============================] - 2s 4ms/step - loss: 2.9802e-11 - accuracy: 1.0000 - val_loss: 0.2882 - val_accuracy: 0.9872
Epoch 118/500
469/469 [==============================] - 2s 4ms/step - loss: 2.7815e-11 - accuracy: 1.0000 - val_loss: 0.2894 - val_accuracy: 0.9872
Epoch 119/500
469/469 [==============================] - 2s 4ms/step - loss: 2.1855e-11 - accuracy: 1.0000 - val_loss: 0.2906 - val_accuracy: 0.9872
Epoch 120/500
469/469 [==============================] - 2s 4ms/step - loss: 1.3908e-11 - accuracy: 1.0000 - val_loss: 0.2917 - val_accuracy: 0.9872
Epoch 121/500
469/469 [==============================] - 2s 4ms/step - loss: 7.9473e-12 - accuracy: 1.0000 - val_loss: 0.2928 - val_accuracy: 0.9872
Epoch 122/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2938 - val_accuracy: 0.9872
Epoch 123/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2947 - val_accuracy: 0.9872
Epoch 124/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9872
Epoch 125/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2966 - val_accuracy: 0.9872
Epoch 126/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9872
Epoch 127/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2983 - val_accuracy: 0.9872
Epoch 128/500
469/469 [==============================] - 2s 4ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2991 - val_accuracy: 0.9872
Epoch 129/500
469/469 [==============================] - 2s 5ms/step - loss: 3.9736e-12 - accuracy: 1.0000 - val_loss: 0.2998 - val_accuracy: 0.9872
Epoch 130/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9871
Epoch 131/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3011 - val_accuracy: 0.9871
Epoch 132/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3017 - val_accuracy: 0.9871
Epoch 133/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3023 - val_accuracy: 0.9871
Epoch 134/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3029 - val_accuracy: 0.9871
Epoch 135/500
469/469 [==============================] - 2s 4ms/step - loss: 1.9868e-12 - accuracy: 1.0000 - val_loss: 0.3035 - val_accuracy: 0.9871
Epoch 136/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3040 - val_accuracy: 0.9871
Epoch 137/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3045 - val_accuracy: 0.9871
Epoch 138/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3049 - val_accuracy: 0.9871
Epoch 139/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3054 - val_accuracy: 0.9871
Epoch 140/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3058 - val_accuracy: 0.9871
Epoch 141/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3063 - val_accuracy: 0.9871
Epoch 142/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3066 - val_accuracy: 0.9871
Epoch 143/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3070 - val_accuracy: 0.9871
Epoch 144/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3074 - val_accuracy: 0.9871
Epoch 145/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3078 - val_accuracy: 0.9871
Epoch 146/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3081 - val_accuracy: 0.9871
Epoch 147/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3085 - val_accuracy: 0.9871
Epoch 148/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3088 - val_accuracy: 0.9871
Epoch 149/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3091 - val_accuracy: 0.9871
Epoch 150/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3094 - val_accuracy: 0.9871
Epoch 151/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3097 - val_accuracy: 0.9871
Epoch 152/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3100 - val_accuracy: 0.9871
Epoch 153/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3103 - val_accuracy: 0.9871
Epoch 154/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3106 - val_accuracy: 0.9871
Epoch 155/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3108 - val_accuracy: 0.9871
Epoch 156/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3111 - val_accuracy: 0.9871
Epoch 157/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3114 - val_accuracy: 0.9871
Epoch 158/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3116 - val_accuracy: 0.9871
Epoch 159/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3118 - val_accuracy: 0.9871
Epoch 160/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3121 - val_accuracy: 0.9871
Epoch 161/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3123 - val_accuracy: 0.9871
Epoch 162/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3126 - val_accuracy: 0.9871
Epoch 163/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3128 - val_accuracy: 0.9871
Epoch 164/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3130 - val_accuracy: 0.9871
Epoch 165/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3132 - val_accuracy: 0.9871
Epoch 166/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3134 - val_accuracy: 0.9871
Epoch 167/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3136 - val_accuracy: 0.9871
Epoch 168/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3138 - val_accuracy: 0.9871
Epoch 169/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3140 - val_accuracy: 0.9871
Epoch 170/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3142 - val_accuracy: 0.9871
Epoch 171/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3144 - val_accuracy: 0.9871
Epoch 172/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3146 - val_accuracy: 0.9871
Epoch 173/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3148 - val_accuracy: 0.9871
Epoch 174/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3150 - val_accuracy: 0.9871
Epoch 175/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3152 - val_accuracy: 0.9871
Epoch 176/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3153 - val_accuracy: 0.9871
Epoch 177/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3155 - val_accuracy: 0.9871
Epoch 178/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3157 - val_accuracy: 0.9871
Epoch 179/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3158 - val_accuracy: 0.9871
Epoch 180/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3160 - val_accuracy: 0.9871
Epoch 181/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3162 - val_accuracy: 0.9871
Epoch 182/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3163 - val_accuracy: 0.9871
Epoch 183/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3165 - val_accuracy: 0.9871
Epoch 184/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3166 - val_accuracy: 0.9871
Epoch 185/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3168 - val_accuracy: 0.9871
Epoch 186/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3169 - val_accuracy: 0.9871
Epoch 187/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3171 - val_accuracy: 0.9871
Epoch 188/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3172 - val_accuracy: 0.9871
Epoch 189/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3174 - val_accuracy: 0.9871
Epoch 190/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3175 - val_accuracy: 0.9871
Epoch 191/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3177 - val_accuracy: 0.9871
Epoch 192/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3178 - val_accuracy: 0.9871
Epoch 193/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3179 - val_accuracy: 0.9871
Epoch 194/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3181 - val_accuracy: 0.9871
Epoch 195/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3182 - val_accuracy: 0.9871
Epoch 196/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3183 - val_accuracy: 0.9871
Epoch 197/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3185 - val_accuracy: 0.9871
Epoch 198/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3186 - val_accuracy: 0.9871
Epoch 199/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3187 - val_accuracy: 0.9871
Epoch 200/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3188 - val_accuracy: 0.9871
Epoch 201/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3190 - val_accuracy: 0.9871
Epoch 202/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3191 - val_accuracy: 0.9871
Epoch 203/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3192 - val_accuracy: 0.9871
Epoch 204/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3193 - val_accuracy: 0.9871
Epoch 205/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3194 - val_accuracy: 0.9871
Epoch 206/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3195 - val_accuracy: 0.9871
Epoch 207/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3197 - val_accuracy: 0.9871
Epoch 208/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3198 - val_accuracy: 0.9871
Epoch 209/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3199 - val_accuracy: 0.9871
Epoch 210/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3200 - val_accuracy: 0.9871
Epoch 211/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3201 - val_accuracy: 0.9871
Epoch 212/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3202 - val_accuracy: 0.9871
Epoch 213/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3203 - val_accuracy: 0.9871
Epoch 214/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.9871
Epoch 215/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3205 - val_accuracy: 0.9871
Epoch 216/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3206 - val_accuracy: 0.9871
Epoch 217/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3207 - val_accuracy: 0.9871
Epoch 218/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3208 - val_accuracy: 0.9871
Epoch 219/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3209 - val_accuracy: 0.9871
Epoch 220/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3210 - val_accuracy: 0.9871
Epoch 221/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3211 - val_accuracy: 0.9871
Epoch 222/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.9871
Epoch 223/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3213 - val_accuracy: 0.9871
Epoch 224/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3214 - val_accuracy: 0.9871
Epoch 225/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3215 - val_accuracy: 0.9871
Epoch 226/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3216 - val_accuracy: 0.9871
Epoch 227/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3217 - val_accuracy: 0.9871
Epoch 228/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3218 - val_accuracy: 0.9871
Epoch 229/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3219 - val_accuracy: 0.9871
Epoch 230/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3220 - val_accuracy: 0.9871
Epoch 231/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3221 - val_accuracy: 0.9871
Epoch 232/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3222 - val_accuracy: 0.9871
Epoch 233/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3222 - val_accuracy: 0.9872
Epoch 234/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3223 - val_accuracy: 0.9872
Epoch 235/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9872
Epoch 236/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3225 - val_accuracy: 0.9872
Epoch 237/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3226 - val_accuracy: 0.9872
Epoch 238/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3227 - val_accuracy: 0.9872
Epoch 239/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3227 - val_accuracy: 0.9872
Epoch 240/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3228 - val_accuracy: 0.9872
Epoch 241/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3229 - val_accuracy: 0.9872
Epoch 242/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3230 - val_accuracy: 0.9872
Epoch 243/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3231 - val_accuracy: 0.9872
Epoch 244/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9872
Epoch 245/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9872
Epoch 246/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3233 - val_accuracy: 0.9872
Epoch 247/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3234 - val_accuracy: 0.9872
Epoch 248/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9872
Epoch 249/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3235 - val_accuracy: 0.9872
Epoch 250/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3236 - val_accuracy: 0.9872
Epoch 251/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9872
Epoch 252/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9872
Epoch 253/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3238 - val_accuracy: 0.9872
Epoch 254/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9872
Epoch 255/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3240 - val_accuracy: 0.9872
Epoch 256/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9872
Epoch 257/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3241 - val_accuracy: 0.9872
Epoch 258/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3242 - val_accuracy: 0.9872
Epoch 259/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9873
Epoch 260/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3243 - val_accuracy: 0.9873
Epoch 261/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9873
Epoch 262/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3245 - val_accuracy: 0.9873
Epoch 263/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9873
Epoch 264/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3246 - val_accuracy: 0.9873
Epoch 265/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3247 - val_accuracy: 0.9873
Epoch 266/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9873
Epoch 267/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9873
Epoch 268/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3249 - val_accuracy: 0.9873
Epoch 269/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9873
Epoch 270/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3250 - val_accuracy: 0.9873
Epoch 271/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9873
Epoch 272/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3251 - val_accuracy: 0.9873
Epoch 273/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3252 - val_accuracy: 0.9873
Epoch 274/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9873
Epoch 275/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9873
Epoch 276/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3254 - val_accuracy: 0.9873
Epoch 277/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9873
Epoch 278/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3255 - val_accuracy: 0.9873
Epoch 279/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9873
Epoch 280/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3256 - val_accuracy: 0.9873
Epoch 281/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3257 - val_accuracy: 0.9873
Epoch 282/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3258 - val_accuracy: 0.9873
Epoch 283/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3258 - val_accuracy: 0.9873
Epoch 284/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9873
Epoch 285/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9873
Epoch 286/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3260 - val_accuracy: 0.9873
Epoch 287/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9873
Epoch 288/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3261 - val_accuracy: 0.9873
Epoch 289/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9873
Epoch 290/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3262 - val_accuracy: 0.9873
Epoch 291/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3263 - val_accuracy: 0.9873
Epoch 292/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9873
Epoch 293/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3264 - val_accuracy: 0.9873
Epoch 294/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9873
Epoch 295/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9873
Epoch 296/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9873
Epoch 297/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9873
Epoch 298/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9873
Epoch 299/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3267 - val_accuracy: 0.9873
Epoch 300/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3268 - val_accuracy: 0.9873
Epoch 301/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9873
Epoch 302/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9873
Epoch 303/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3270 - val_accuracy: 0.9873
Epoch 304/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3270 - val_accuracy: 0.9873
Epoch 305/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3271 - val_accuracy: 0.9873
Epoch 306/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3271 - val_accuracy: 0.9873
Epoch 307/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3272 - val_accuracy: 0.9873
Epoch 308/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3272 - val_accuracy: 0.9873
Epoch 309/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3273 - val_accuracy: 0.9873
Epoch 310/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3273 - val_accuracy: 0.9873
Epoch 311/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3274 - val_accuracy: 0.9873
Epoch 312/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3274 - val_accuracy: 0.9873
Epoch 313/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3275 - val_accuracy: 0.9873
Epoch 314/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3275 - val_accuracy: 0.9873
Epoch 315/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3276 - val_accuracy: 0.9873
Epoch 316/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3276 - val_accuracy: 0.9873
Epoch 317/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3277 - val_accuracy: 0.9873
Epoch 318/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3277 - val_accuracy: 0.9873
Epoch 319/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3278 - val_accuracy: 0.9873
Epoch 320/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3278 - val_accuracy: 0.9873
Epoch 321/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3279 - val_accuracy: 0.9873
Epoch 322/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3279 - val_accuracy: 0.9873
Epoch 323/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3280 - val_accuracy: 0.9873
Epoch 324/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3280 - val_accuracy: 0.9873
Epoch 325/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3281 - val_accuracy: 0.9873
Epoch 326/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3281 - val_accuracy: 0.9873
Epoch 327/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3281 - val_accuracy: 0.9873
Epoch 328/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3282 - val_accuracy: 0.9873
Epoch 329/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3282 - val_accuracy: 0.9873
Epoch 330/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3283 - val_accuracy: 0.9873
Epoch 331/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3283 - val_accuracy: 0.9873
Epoch 332/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3284 - val_accuracy: 0.9873
Epoch 333/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3284 - val_accuracy: 0.9873
Epoch 334/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3285 - val_accuracy: 0.9873
Epoch 335/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3285 - val_accuracy: 0.9873
Epoch 336/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3286 - val_accuracy: 0.9873
Epoch 337/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3286 - val_accuracy: 0.9873
Epoch 338/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3287 - val_accuracy: 0.9873
Epoch 339/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3287 - val_accuracy: 0.9873
Epoch 340/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3287 - val_accuracy: 0.9873
Epoch 341/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3288 - val_accuracy: 0.9873
Epoch 342/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3288 - val_accuracy: 0.9873
Epoch 343/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3289 - val_accuracy: 0.9873
Epoch 344/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3289 - val_accuracy: 0.9873
Epoch 345/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3290 - val_accuracy: 0.9873
Epoch 346/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3290 - val_accuracy: 0.9873
Epoch 347/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3290 - val_accuracy: 0.9873
Epoch 348/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9873
Epoch 349/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9873
Epoch 350/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3292 - val_accuracy: 0.9873
Epoch 351/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3292 - val_accuracy: 0.9873
Epoch 352/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3292 - val_accuracy: 0.9873
Epoch 353/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3293 - val_accuracy: 0.9873
Epoch 354/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3293 - val_accuracy: 0.9873
Epoch 355/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3294 - val_accuracy: 0.9873
Epoch 356/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3294 - val_accuracy: 0.9873
Epoch 357/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3295 - val_accuracy: 0.9873
Epoch 358/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3295 - val_accuracy: 0.9873
Epoch 359/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3295 - val_accuracy: 0.9873
Epoch 360/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3296 - val_accuracy: 0.9873
Epoch 361/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3296 - val_accuracy: 0.9873
Epoch 362/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3297 - val_accuracy: 0.9873
Epoch 363/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3297 - val_accuracy: 0.9873
Epoch 364/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3297 - val_accuracy: 0.9873
Epoch 365/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3298 - val_accuracy: 0.9873
Epoch 366/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3298 - val_accuracy: 0.9873
Epoch 367/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3299 - val_accuracy: 0.9873
Epoch 368/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3299 - val_accuracy: 0.9873
Epoch 369/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3299 - val_accuracy: 0.9873
Epoch 370/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3300 - val_accuracy: 0.9874
Epoch 371/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3300 - val_accuracy: 0.9874
Epoch 372/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9874
Epoch 373/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9874
Epoch 374/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3301 - val_accuracy: 0.9874
Epoch 375/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3302 - val_accuracy: 0.9874
Epoch 376/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3302 - val_accuracy: 0.9874
Epoch 377/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3302 - val_accuracy: 0.9874
Epoch 378/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3303 - val_accuracy: 0.9874
Epoch 379/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3303 - val_accuracy: 0.9874
Epoch 380/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3304 - val_accuracy: 0.9874
Epoch 381/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3304 - val_accuracy: 0.9874
Epoch 382/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3304 - val_accuracy: 0.9874
Epoch 383/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3305 - val_accuracy: 0.9874
Epoch 384/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3305 - val_accuracy: 0.9874
Epoch 385/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3305 - val_accuracy: 0.9874
Epoch 386/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3306 - val_accuracy: 0.9874
Epoch 387/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3306 - val_accuracy: 0.9874
Epoch 388/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3306 - val_accuracy: 0.9874
Epoch 389/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3307 - val_accuracy: 0.9874
Epoch 390/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3307 - val_accuracy: 0.9874
Epoch 391/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3308 - val_accuracy: 0.9874
Epoch 392/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3308 - val_accuracy: 0.9874
Epoch 393/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3308 - val_accuracy: 0.9874
Epoch 394/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3309 - val_accuracy: 0.9874
Epoch 395/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3309 - val_accuracy: 0.9874
Epoch 396/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3309 - val_accuracy: 0.9874
Epoch 397/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3310 - val_accuracy: 0.9874
Epoch 398/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3310 - val_accuracy: 0.9874
Epoch 399/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3310 - val_accuracy: 0.9874
Epoch 400/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3311 - val_accuracy: 0.9874
Epoch 401/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3311 - val_accuracy: 0.9874
Epoch 402/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3311 - val_accuracy: 0.9874
Epoch 403/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3312 - val_accuracy: 0.9874
Epoch 404/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3312 - val_accuracy: 0.9874
Epoch 405/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3312 - val_accuracy: 0.9874
Epoch 406/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3313 - val_accuracy: 0.9874
Epoch 407/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3313 - val_accuracy: 0.9874
Epoch 408/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3313 - val_accuracy: 0.9874
Epoch 409/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3314 - val_accuracy: 0.9874
Epoch 410/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3314 - val_accuracy: 0.9874
Epoch 411/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3314 - val_accuracy: 0.9874
Epoch 412/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3315 - val_accuracy: 0.9874
Epoch 413/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3315 - val_accuracy: 0.9874
Epoch 414/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3315 - val_accuracy: 0.9874
Epoch 415/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3316 - val_accuracy: 0.9874
Epoch 416/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3316 - val_accuracy: 0.9874
Epoch 417/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3316 - val_accuracy: 0.9874
Epoch 418/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3317 - val_accuracy: 0.9874
Epoch 419/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3317 - val_accuracy: 0.9874
Epoch 420/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3317 - val_accuracy: 0.9874
Epoch 421/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3318 - val_accuracy: 0.9874
Epoch 422/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3318 - val_accuracy: 0.9874
Epoch 423/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3318 - val_accuracy: 0.9874
Epoch 424/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3319 - val_accuracy: 0.9874
Epoch 425/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3319 - val_accuracy: 0.9874
Epoch 426/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3319 - val_accuracy: 0.9874
Epoch 427/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3320 - val_accuracy: 0.9874
Epoch 428/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3320 - val_accuracy: 0.9874
Epoch 429/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3320 - val_accuracy: 0.9874
Epoch 430/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3321 - val_accuracy: 0.9874
Epoch 431/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3321 - val_accuracy: 0.9874
Epoch 432/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3321 - val_accuracy: 0.9874
Epoch 433/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3321 - val_accuracy: 0.9874
Epoch 434/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9874
Epoch 435/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9874
Epoch 436/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9874
Epoch 437/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3323 - val_accuracy: 0.9874
Epoch 438/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3323 - val_accuracy: 0.9874
Epoch 439/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3323 - val_accuracy: 0.9874
Epoch 440/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3324 - val_accuracy: 0.9874
Epoch 441/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3324 - val_accuracy: 0.9874
Epoch 442/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3324 - val_accuracy: 0.9874
Epoch 443/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3325 - val_accuracy: 0.9874
Epoch 444/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3325 - val_accuracy: 0.9874
Epoch 445/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3325 - val_accuracy: 0.9874
Epoch 446/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3325 - val_accuracy: 0.9874
Epoch 447/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.9874
Epoch 448/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.9874
Epoch 449/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.9874
Epoch 450/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9874
Epoch 451/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9874
Epoch 452/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9874
Epoch 453/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3327 - val_accuracy: 0.9874
Epoch 454/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3328 - val_accuracy: 0.9874
Epoch 455/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3328 - val_accuracy: 0.9874
Epoch 456/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3328 - val_accuracy: 0.9874
Epoch 457/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3329 - val_accuracy: 0.9874
Epoch 458/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3329 - val_accuracy: 0.9874
Epoch 459/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3329 - val_accuracy: 0.9874
Epoch 460/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3329 - val_accuracy: 0.9874
Epoch 461/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9874
Epoch 462/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9874
Epoch 463/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9874
Epoch 464/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 0.9874
Epoch 465/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 0.9874
Epoch 466/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 0.9874
Epoch 467/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3331 - val_accuracy: 0.9874
Epoch 468/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3332 - val_accuracy: 0.9874
Epoch 469/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3332 - val_accuracy: 0.9874
Epoch 470/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3332 - val_accuracy: 0.9874
Epoch 471/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3332 - val_accuracy: 0.9874
Epoch 472/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3333 - val_accuracy: 0.9874
Epoch 473/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3333 - val_accuracy: 0.9874
Epoch 474/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3333 - val_accuracy: 0.9874
Epoch 475/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3334 - val_accuracy: 0.9874
Epoch 476/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3334 - val_accuracy: 0.9874
Epoch 477/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3334 - val_accuracy: 0.9874
Epoch 478/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3334 - val_accuracy: 0.9874
Epoch 479/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9874
Epoch 480/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9874
Epoch 481/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9874
Epoch 482/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3335 - val_accuracy: 0.9874
Epoch 483/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3336 - val_accuracy: 0.9874
Epoch 484/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3336 - val_accuracy: 0.9874
Epoch 485/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3336 - val_accuracy: 0.9874
Epoch 486/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3336 - val_accuracy: 0.9874
Epoch 487/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3337 - val_accuracy: 0.9874
Epoch 488/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3337 - val_accuracy: 0.9874
Epoch 489/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3337 - val_accuracy: 0.9874
Epoch 490/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3338 - val_accuracy: 0.9874
Epoch 491/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3338 - val_accuracy: 0.9874
Epoch 492/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3338 - val_accuracy: 0.9874
Epoch 493/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3338 - val_accuracy: 0.9874
Epoch 494/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3339 - val_accuracy: 0.9874
Epoch 495/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3339 - val_accuracy: 0.9874
Epoch 496/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3339 - val_accuracy: 0.9874
Epoch 497/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3339 - val_accuracy: 0.9874
Epoch 498/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3340 - val_accuracy: 0.9874
Epoch 499/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3340 - val_accuracy: 0.9874
Epoch 500/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0000e+00 - accuracy: 1.0000 - val_loss: 0.3340 - val_accuracy: 0.9874

Activation function: ReLU; initialization: Kaiming He’s initializer; with dropout rate: 0.2 for the first layer and 0.5 for the other hidden layers

In [ ]:
shape = (28, 28) # Define shape of input for Keras model

init = tf.keras.initializers.HeNormal(seed=None)

model = keras.Sequential(
    [
        tf.keras.layers.Input(shape=shape),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1024,activation='relu',kernel_initializer=init),
        tf.keras.layers.Dense(10, activation='softmax', kernel_initializer=init)
    ]
)

model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 784)               0         
_________________________________________________________________
dropout (Dropout)            (None, 784)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 1024)              803840    
_________________________________________________________________
dropout_1 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_7 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dropout_2 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_8 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dropout_3 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 1024)              1049600   
_________________________________________________________________
dropout_4 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_10 (Dense)             (None, 1024)              1049600   
_________________________________________________________________
dense_11 (Dense)             (None, 10)                10250     
=================================================================
Total params: 5,012,490
Trainable params: 5,012,490
Non-trainable params: 0
_________________________________________________________________
In [ ]:
opt = keras.optimizers.Adam()
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
historyd1 = model.fit(X_train, y_train, batch_size=128, epochs=500, validation_data=(X_test, y_test))
Epoch 1/500
469/469 [==============================] - 2s 5ms/step - loss: 0.6479 - accuracy: 0.7908 - val_loss: 0.1693 - val_accuracy: 0.9496
Epoch 2/500
469/469 [==============================] - 2s 4ms/step - loss: 0.2751 - accuracy: 0.9209 - val_loss: 0.1315 - val_accuracy: 0.9623
Epoch 3/500
469/469 [==============================] - 2s 4ms/step - loss: 0.2158 - accuracy: 0.9384 - val_loss: 0.1165 - val_accuracy: 0.9685
Epoch 4/500
469/469 [==============================] - 2s 5ms/step - loss: 0.1927 - accuracy: 0.9469 - val_loss: 0.0935 - val_accuracy: 0.9730
Epoch 5/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1760 - accuracy: 0.9513 - val_loss: 0.0990 - val_accuracy: 0.9713
Epoch 6/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1542 - accuracy: 0.9571 - val_loss: 0.0919 - val_accuracy: 0.9744
Epoch 7/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1487 - accuracy: 0.9599 - val_loss: 0.0844 - val_accuracy: 0.9766
Epoch 8/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1421 - accuracy: 0.9610 - val_loss: 0.0868 - val_accuracy: 0.9775
Epoch 9/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1322 - accuracy: 0.9642 - val_loss: 0.0804 - val_accuracy: 0.9793
Epoch 10/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1303 - accuracy: 0.9652 - val_loss: 0.0819 - val_accuracy: 0.9789
Epoch 11/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1248 - accuracy: 0.9668 - val_loss: 0.0845 - val_accuracy: 0.9786
Epoch 12/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1163 - accuracy: 0.9688 - val_loss: 0.0761 - val_accuracy: 0.9794
Epoch 13/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1180 - accuracy: 0.9686 - val_loss: 0.0745 - val_accuracy: 0.9821
Epoch 14/500
469/469 [==============================] - 2s 5ms/step - loss: 0.1127 - accuracy: 0.9694 - val_loss: 0.0754 - val_accuracy: 0.9816
Epoch 15/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1077 - accuracy: 0.9717 - val_loss: 0.0747 - val_accuracy: 0.9803
Epoch 16/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1090 - accuracy: 0.9707 - val_loss: 0.0712 - val_accuracy: 0.9826
Epoch 17/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1051 - accuracy: 0.9725 - val_loss: 0.0749 - val_accuracy: 0.9809
Epoch 18/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1020 - accuracy: 0.9728 - val_loss: 0.0716 - val_accuracy: 0.9802
Epoch 19/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0974 - accuracy: 0.9742 - val_loss: 0.0766 - val_accuracy: 0.9820
Epoch 20/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1045 - accuracy: 0.9732 - val_loss: 0.0761 - val_accuracy: 0.9819
Epoch 21/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1003 - accuracy: 0.9742 - val_loss: 0.0674 - val_accuracy: 0.9837
Epoch 22/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1001 - accuracy: 0.9746 - val_loss: 0.0738 - val_accuracy: 0.9826
Epoch 23/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0955 - accuracy: 0.9754 - val_loss: 0.0719 - val_accuracy: 0.9836
Epoch 24/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0926 - accuracy: 0.9757 - val_loss: 0.0661 - val_accuracy: 0.9834
Epoch 25/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0917 - accuracy: 0.9760 - val_loss: 0.0678 - val_accuracy: 0.9835
Epoch 26/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0873 - accuracy: 0.9772 - val_loss: 0.0761 - val_accuracy: 0.9839
Epoch 27/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0878 - accuracy: 0.9782 - val_loss: 0.0731 - val_accuracy: 0.9810
Epoch 28/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0887 - accuracy: 0.9771 - val_loss: 0.0763 - val_accuracy: 0.9824
Epoch 29/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0799 - accuracy: 0.9783 - val_loss: 0.0649 - val_accuracy: 0.9845
Epoch 30/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0785 - accuracy: 0.9790 - val_loss: 0.0733 - val_accuracy: 0.9825
Epoch 31/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0815 - accuracy: 0.9795 - val_loss: 0.0721 - val_accuracy: 0.9843
Epoch 32/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0827 - accuracy: 0.9785 - val_loss: 0.0653 - val_accuracy: 0.9836
Epoch 33/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0822 - accuracy: 0.9796 - val_loss: 0.0720 - val_accuracy: 0.9835
Epoch 34/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0804 - accuracy: 0.9797 - val_loss: 0.0621 - val_accuracy: 0.9852
Epoch 35/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0768 - accuracy: 0.9798 - val_loss: 0.0731 - val_accuracy: 0.9847
Epoch 36/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0834 - accuracy: 0.9797 - val_loss: 0.0693 - val_accuracy: 0.9858
Epoch 37/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0770 - accuracy: 0.9808 - val_loss: 0.0689 - val_accuracy: 0.9856
Epoch 38/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0756 - accuracy: 0.9812 - val_loss: 0.0731 - val_accuracy: 0.9838
Epoch 39/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0742 - accuracy: 0.9806 - val_loss: 0.0650 - val_accuracy: 0.9853
Epoch 40/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0763 - accuracy: 0.9812 - val_loss: 0.0778 - val_accuracy: 0.9838
Epoch 41/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0753 - accuracy: 0.9810 - val_loss: 0.0733 - val_accuracy: 0.9852
Epoch 42/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0706 - accuracy: 0.9819 - val_loss: 0.0738 - val_accuracy: 0.9843
Epoch 43/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0705 - accuracy: 0.9824 - val_loss: 0.0712 - val_accuracy: 0.9842
Epoch 44/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0724 - accuracy: 0.9829 - val_loss: 0.0702 - val_accuracy: 0.9848
Epoch 45/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0694 - accuracy: 0.9822 - val_loss: 0.0733 - val_accuracy: 0.9854
Epoch 46/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0724 - accuracy: 0.9821 - val_loss: 0.0718 - val_accuracy: 0.9846
Epoch 47/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0782 - accuracy: 0.9819 - val_loss: 0.0729 - val_accuracy: 0.9857
Epoch 48/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0731 - accuracy: 0.9821 - val_loss: 0.0732 - val_accuracy: 0.9848
Epoch 49/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0671 - accuracy: 0.9840 - val_loss: 0.0701 - val_accuracy: 0.9856
Epoch 50/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0656 - accuracy: 0.9833 - val_loss: 0.0642 - val_accuracy: 0.9864
Epoch 51/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0700 - accuracy: 0.9832 - val_loss: 0.0725 - val_accuracy: 0.9845
Epoch 52/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0646 - accuracy: 0.9841 - val_loss: 0.0756 - val_accuracy: 0.9848
Epoch 53/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0688 - accuracy: 0.9837 - val_loss: 0.0789 - val_accuracy: 0.9865
Epoch 54/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0674 - accuracy: 0.9841 - val_loss: 0.0671 - val_accuracy: 0.9859
Epoch 55/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0659 - accuracy: 0.9835 - val_loss: 0.0669 - val_accuracy: 0.9870
Epoch 56/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0643 - accuracy: 0.9840 - val_loss: 0.0796 - val_accuracy: 0.9857
Epoch 57/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0655 - accuracy: 0.9842 - val_loss: 0.0704 - val_accuracy: 0.9851
Epoch 58/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0629 - accuracy: 0.9848 - val_loss: 0.0648 - val_accuracy: 0.9871
Epoch 59/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0617 - accuracy: 0.9846 - val_loss: 0.0645 - val_accuracy: 0.9864
Epoch 60/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0586 - accuracy: 0.9856 - val_loss: 0.0707 - val_accuracy: 0.9870
Epoch 61/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0691 - accuracy: 0.9838 - val_loss: 0.0706 - val_accuracy: 0.9865
Epoch 62/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0644 - accuracy: 0.9845 - val_loss: 0.0799 - val_accuracy: 0.9852
Epoch 63/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0602 - accuracy: 0.9854 - val_loss: 0.0774 - val_accuracy: 0.9849
Epoch 64/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0643 - accuracy: 0.9845 - val_loss: 0.0799 - val_accuracy: 0.9846
Epoch 65/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0624 - accuracy: 0.9859 - val_loss: 0.0659 - val_accuracy: 0.9862
Epoch 66/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0677 - accuracy: 0.9845 - val_loss: 0.0640 - val_accuracy: 0.9861
Epoch 67/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0679 - accuracy: 0.9851 - val_loss: 0.0773 - val_accuracy: 0.9865
Epoch 68/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0644 - accuracy: 0.9855 - val_loss: 0.0657 - val_accuracy: 0.9869
Epoch 69/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0635 - accuracy: 0.9848 - val_loss: 0.0703 - val_accuracy: 0.9854
Epoch 70/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0600 - accuracy: 0.9856 - val_loss: 0.0670 - val_accuracy: 0.9853
Epoch 71/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0574 - accuracy: 0.9859 - val_loss: 0.0778 - val_accuracy: 0.9856
Epoch 72/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9868 - val_loss: 0.0673 - val_accuracy: 0.9868
Epoch 73/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.9866 - val_loss: 0.0688 - val_accuracy: 0.9868
Epoch 74/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0598 - accuracy: 0.9866 - val_loss: 0.0613 - val_accuracy: 0.9874
Epoch 75/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0638 - accuracy: 0.9853 - val_loss: 0.0670 - val_accuracy: 0.9868
Epoch 76/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0635 - accuracy: 0.9857 - val_loss: 0.0583 - val_accuracy: 0.9865
Epoch 77/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0630 - accuracy: 0.9856 - val_loss: 0.0649 - val_accuracy: 0.9855
Epoch 78/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0589 - accuracy: 0.9865 - val_loss: 0.0676 - val_accuracy: 0.9852
Epoch 79/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0580 - accuracy: 0.9868 - val_loss: 0.0763 - val_accuracy: 0.9859
Epoch 80/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0645 - accuracy: 0.9859 - val_loss: 0.0714 - val_accuracy: 0.9864
Epoch 81/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9877 - val_loss: 0.0664 - val_accuracy: 0.9857
Epoch 82/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0583 - accuracy: 0.9870 - val_loss: 0.0644 - val_accuracy: 0.9870
Epoch 83/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0627 - accuracy: 0.9865 - val_loss: 0.0766 - val_accuracy: 0.9861
Epoch 84/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0613 - accuracy: 0.9859 - val_loss: 0.0760 - val_accuracy: 0.9862
Epoch 85/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0619 - accuracy: 0.9866 - val_loss: 0.0683 - val_accuracy: 0.9856
Epoch 86/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9874 - val_loss: 0.0717 - val_accuracy: 0.9866
Epoch 87/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0560 - accuracy: 0.9868 - val_loss: 0.0791 - val_accuracy: 0.9871
Epoch 88/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9880 - val_loss: 0.0749 - val_accuracy: 0.9851
Epoch 89/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9879 - val_loss: 0.0719 - val_accuracy: 0.9851
Epoch 90/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0620 - accuracy: 0.9862 - val_loss: 0.0694 - val_accuracy: 0.9856
Epoch 91/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0597 - accuracy: 0.9869 - val_loss: 0.0673 - val_accuracy: 0.9855
Epoch 92/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0532 - accuracy: 0.9878 - val_loss: 0.0692 - val_accuracy: 0.9862
Epoch 93/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0550 - accuracy: 0.9872 - val_loss: 0.0765 - val_accuracy: 0.9854
Epoch 94/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0547 - accuracy: 0.9876 - val_loss: 0.0647 - val_accuracy: 0.9871
Epoch 95/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0568 - accuracy: 0.9878 - val_loss: 0.0677 - val_accuracy: 0.9846
Epoch 96/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0546 - accuracy: 0.9875 - val_loss: 0.0777 - val_accuracy: 0.9851
Epoch 97/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0542 - accuracy: 0.9879 - val_loss: 0.0729 - val_accuracy: 0.9835
Epoch 98/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0620 - accuracy: 0.9872 - val_loss: 0.0670 - val_accuracy: 0.9853
Epoch 99/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.9869 - val_loss: 0.0674 - val_accuracy: 0.9864
Epoch 100/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0597 - accuracy: 0.9876 - val_loss: 0.0680 - val_accuracy: 0.9847
Epoch 101/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0517 - accuracy: 0.9887 - val_loss: 0.0693 - val_accuracy: 0.9874
Epoch 102/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0534 - accuracy: 0.9878 - val_loss: 0.0717 - val_accuracy: 0.9858
Epoch 103/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0564 - accuracy: 0.9871 - val_loss: 0.0693 - val_accuracy: 0.9862
Epoch 104/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0519 - accuracy: 0.9882 - val_loss: 0.0658 - val_accuracy: 0.9851
Epoch 105/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0545 - accuracy: 0.9885 - val_loss: 0.0694 - val_accuracy: 0.9868
Epoch 106/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0617 - accuracy: 0.9878 - val_loss: 0.0693 - val_accuracy: 0.9847
Epoch 107/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0563 - accuracy: 0.9877 - val_loss: 0.0669 - val_accuracy: 0.9856
Epoch 108/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0567 - accuracy: 0.9881 - val_loss: 0.0772 - val_accuracy: 0.9849
Epoch 109/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0504 - accuracy: 0.9886 - val_loss: 0.0681 - val_accuracy: 0.9868
Epoch 110/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0573 - accuracy: 0.9877 - val_loss: 0.0693 - val_accuracy: 0.9864
Epoch 111/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0503 - accuracy: 0.9885 - val_loss: 0.0859 - val_accuracy: 0.9854
Epoch 112/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0515 - accuracy: 0.9887 - val_loss: 0.0656 - val_accuracy: 0.9868
Epoch 113/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0583 - accuracy: 0.9878 - val_loss: 0.0687 - val_accuracy: 0.9865
Epoch 114/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9879 - val_loss: 0.0622 - val_accuracy: 0.9876
Epoch 115/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9876 - val_loss: 0.0779 - val_accuracy: 0.9869
Epoch 116/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0517 - accuracy: 0.9887 - val_loss: 0.0701 - val_accuracy: 0.9870
Epoch 117/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0534 - accuracy: 0.9891 - val_loss: 0.0767 - val_accuracy: 0.9864
Epoch 118/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0561 - accuracy: 0.9881 - val_loss: 0.0652 - val_accuracy: 0.9866
Epoch 119/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9883 - val_loss: 0.0668 - val_accuracy: 0.9864
Epoch 120/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0546 - accuracy: 0.9882 - val_loss: 0.0646 - val_accuracy: 0.9873
Epoch 121/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0566 - accuracy: 0.9883 - val_loss: 0.0740 - val_accuracy: 0.9855
Epoch 122/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0525 - accuracy: 0.9891 - val_loss: 0.0723 - val_accuracy: 0.9873
Epoch 123/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0567 - accuracy: 0.9883 - val_loss: 0.0672 - val_accuracy: 0.9865
Epoch 124/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0571 - accuracy: 0.9893 - val_loss: 0.0696 - val_accuracy: 0.9855
Epoch 125/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0554 - accuracy: 0.9889 - val_loss: 0.0752 - val_accuracy: 0.9852
Epoch 126/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0642 - accuracy: 0.9884 - val_loss: 0.0738 - val_accuracy: 0.9837
Epoch 127/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0541 - accuracy: 0.9890 - val_loss: 0.0704 - val_accuracy: 0.9862
Epoch 128/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0532 - accuracy: 0.9892 - val_loss: 0.0813 - val_accuracy: 0.9853
Epoch 129/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0609 - accuracy: 0.9885 - val_loss: 0.0739 - val_accuracy: 0.9862
Epoch 130/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0548 - accuracy: 0.9889 - val_loss: 0.0686 - val_accuracy: 0.9872
Epoch 131/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0597 - accuracy: 0.9895 - val_loss: 0.0757 - val_accuracy: 0.9859
Epoch 132/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0507 - accuracy: 0.9885 - val_loss: 0.0707 - val_accuracy: 0.9852
Epoch 133/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9890 - val_loss: 0.0659 - val_accuracy: 0.9864
Epoch 134/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0527 - accuracy: 0.9894 - val_loss: 0.0711 - val_accuracy: 0.9850
Epoch 135/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9892 - val_loss: 0.0721 - val_accuracy: 0.9854
Epoch 136/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9897 - val_loss: 0.0713 - val_accuracy: 0.9870
Epoch 137/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0519 - accuracy: 0.9890 - val_loss: 0.0815 - val_accuracy: 0.9869
Epoch 138/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0553 - accuracy: 0.9883 - val_loss: 0.0826 - val_accuracy: 0.9859
Epoch 139/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0529 - accuracy: 0.9892 - val_loss: 0.0734 - val_accuracy: 0.9846
Epoch 140/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0584 - accuracy: 0.9886 - val_loss: 0.0821 - val_accuracy: 0.9860
Epoch 141/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0553 - accuracy: 0.9886 - val_loss: 0.0722 - val_accuracy: 0.9869
Epoch 142/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0647 - accuracy: 0.9882 - val_loss: 0.0758 - val_accuracy: 0.9859
Epoch 143/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0597 - accuracy: 0.9886 - val_loss: 0.0726 - val_accuracy: 0.9860
Epoch 144/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9895 - val_loss: 0.0734 - val_accuracy: 0.9865
Epoch 145/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0576 - accuracy: 0.9891 - val_loss: 0.0769 - val_accuracy: 0.9857
Epoch 146/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0640 - accuracy: 0.9880 - val_loss: 0.0752 - val_accuracy: 0.9861
Epoch 147/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9899 - val_loss: 0.0699 - val_accuracy: 0.9871
Epoch 148/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0556 - accuracy: 0.9894 - val_loss: 0.0709 - val_accuracy: 0.9852
Epoch 149/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0555 - accuracy: 0.9893 - val_loss: 0.0732 - val_accuracy: 0.9866
Epoch 150/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0553 - accuracy: 0.9895 - val_loss: 0.0849 - val_accuracy: 0.9860
Epoch 151/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9899 - val_loss: 0.0760 - val_accuracy: 0.9868
Epoch 152/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0621 - accuracy: 0.9893 - val_loss: 0.0859 - val_accuracy: 0.9863
Epoch 153/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0622 - accuracy: 0.9882 - val_loss: 0.0726 - val_accuracy: 0.9854
Epoch 154/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0545 - accuracy: 0.9894 - val_loss: 0.0829 - val_accuracy: 0.9854
Epoch 155/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.9890 - val_loss: 0.0774 - val_accuracy: 0.9854
Epoch 156/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0499 - accuracy: 0.9898 - val_loss: 0.0914 - val_accuracy: 0.9864
Epoch 157/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0512 - accuracy: 0.9892 - val_loss: 0.0742 - val_accuracy: 0.9870
Epoch 158/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0543 - accuracy: 0.9897 - val_loss: 0.0753 - val_accuracy: 0.9875
Epoch 159/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0469 - accuracy: 0.9910 - val_loss: 0.0715 - val_accuracy: 0.9864
Epoch 160/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0496 - accuracy: 0.9899 - val_loss: 0.0813 - val_accuracy: 0.9874
Epoch 161/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9894 - val_loss: 0.0748 - val_accuracy: 0.9859
Epoch 162/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0499 - accuracy: 0.9895 - val_loss: 0.0772 - val_accuracy: 0.9865
Epoch 163/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0465 - accuracy: 0.9902 - val_loss: 0.0772 - val_accuracy: 0.9861
Epoch 164/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0613 - accuracy: 0.9891 - val_loss: 0.0971 - val_accuracy: 0.9858
Epoch 165/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0665 - accuracy: 0.9891 - val_loss: 0.0859 - val_accuracy: 0.9860
Epoch 166/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9897 - val_loss: 0.0858 - val_accuracy: 0.9854
Epoch 167/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0571 - accuracy: 0.9894 - val_loss: 0.0821 - val_accuracy: 0.9870
Epoch 168/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9906 - val_loss: 0.0753 - val_accuracy: 0.9873
Epoch 169/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0598 - accuracy: 0.9896 - val_loss: 0.0803 - val_accuracy: 0.9859
Epoch 170/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0547 - accuracy: 0.9896 - val_loss: 0.0699 - val_accuracy: 0.9852
Epoch 171/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0484 - accuracy: 0.9901 - val_loss: 0.0729 - val_accuracy: 0.9854
Epoch 172/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0472 - accuracy: 0.9905 - val_loss: 0.0777 - val_accuracy: 0.9866
Epoch 173/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0536 - accuracy: 0.9901 - val_loss: 0.0778 - val_accuracy: 0.9845
Epoch 174/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9896 - val_loss: 0.0942 - val_accuracy: 0.9855
Epoch 175/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0620 - accuracy: 0.9897 - val_loss: 0.0912 - val_accuracy: 0.9855
Epoch 176/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0612 - accuracy: 0.9888 - val_loss: 0.0814 - val_accuracy: 0.9865
Epoch 177/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0511 - accuracy: 0.9900 - val_loss: 0.0795 - val_accuracy: 0.9861
Epoch 178/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0558 - accuracy: 0.9895 - val_loss: 0.0778 - val_accuracy: 0.9864
Epoch 179/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9894 - val_loss: 0.0689 - val_accuracy: 0.9887
Epoch 180/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0463 - accuracy: 0.9908 - val_loss: 0.0771 - val_accuracy: 0.9863
Epoch 181/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0606 - accuracy: 0.9891 - val_loss: 0.0810 - val_accuracy: 0.9869
Epoch 182/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0480 - accuracy: 0.9903 - val_loss: 0.1135 - val_accuracy: 0.9854
Epoch 183/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0619 - accuracy: 0.9891 - val_loss: 0.0744 - val_accuracy: 0.9848
Epoch 184/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9901 - val_loss: 0.0749 - val_accuracy: 0.9867
Epoch 185/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0480 - accuracy: 0.9901 - val_loss: 0.0870 - val_accuracy: 0.9864
Epoch 186/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0485 - accuracy: 0.9910 - val_loss: 0.0902 - val_accuracy: 0.9865
Epoch 187/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9907 - val_loss: 0.0758 - val_accuracy: 0.9860
Epoch 188/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9901 - val_loss: 0.0875 - val_accuracy: 0.9874
Epoch 189/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0551 - accuracy: 0.9904 - val_loss: 0.0714 - val_accuracy: 0.9876
Epoch 190/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0596 - accuracy: 0.9904 - val_loss: 0.0843 - val_accuracy: 0.9860
Epoch 191/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0613 - accuracy: 0.9896 - val_loss: 0.0836 - val_accuracy: 0.9862
Epoch 192/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0554 - accuracy: 0.9898 - val_loss: 0.0898 - val_accuracy: 0.9866
Epoch 193/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0516 - accuracy: 0.9908 - val_loss: 0.0896 - val_accuracy: 0.9864
Epoch 194/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0576 - accuracy: 0.9901 - val_loss: 0.0837 - val_accuracy: 0.9869
Epoch 195/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0552 - accuracy: 0.9905 - val_loss: 0.0785 - val_accuracy: 0.9870
Epoch 196/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0509 - accuracy: 0.9905 - val_loss: 0.0881 - val_accuracy: 0.9850
Epoch 197/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0499 - accuracy: 0.9907 - val_loss: 0.0891 - val_accuracy: 0.9866
Epoch 198/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0453 - accuracy: 0.9908 - val_loss: 0.0854 - val_accuracy: 0.9867
Epoch 199/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0490 - accuracy: 0.9910 - val_loss: 0.0809 - val_accuracy: 0.9859
Epoch 200/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9907 - val_loss: 0.0912 - val_accuracy: 0.9856
Epoch 201/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0494 - accuracy: 0.9903 - val_loss: 0.0871 - val_accuracy: 0.9864
Epoch 202/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0551 - accuracy: 0.9904 - val_loss: 0.0737 - val_accuracy: 0.9867
Epoch 203/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9904 - val_loss: 0.0837 - val_accuracy: 0.9867
Epoch 204/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9906 - val_loss: 0.0822 - val_accuracy: 0.9868
Epoch 205/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0491 - accuracy: 0.9910 - val_loss: 0.0821 - val_accuracy: 0.9868
Epoch 206/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0466 - accuracy: 0.9911 - val_loss: 0.0737 - val_accuracy: 0.9869
Epoch 207/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0486 - accuracy: 0.9906 - val_loss: 0.0806 - val_accuracy: 0.9864
Epoch 208/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0569 - accuracy: 0.9907 - val_loss: 0.0887 - val_accuracy: 0.9852
Epoch 209/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0521 - accuracy: 0.9910 - val_loss: 0.0753 - val_accuracy: 0.9870
Epoch 210/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0528 - accuracy: 0.9905 - val_loss: 0.0911 - val_accuracy: 0.9850
Epoch 211/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0588 - accuracy: 0.9903 - val_loss: 0.0844 - val_accuracy: 0.9858
Epoch 212/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0608 - accuracy: 0.9905 - val_loss: 0.0898 - val_accuracy: 0.9866
Epoch 213/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0409 - accuracy: 0.9917 - val_loss: 0.0772 - val_accuracy: 0.9865
Epoch 214/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0560 - accuracy: 0.9898 - val_loss: 0.0724 - val_accuracy: 0.9877
Epoch 215/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0506 - accuracy: 0.9908 - val_loss: 0.0741 - val_accuracy: 0.9862
Epoch 216/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0468 - accuracy: 0.9912 - val_loss: 0.0937 - val_accuracy: 0.9858
Epoch 217/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0545 - accuracy: 0.9900 - val_loss: 0.0875 - val_accuracy: 0.9856
Epoch 218/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0516 - accuracy: 0.9907 - val_loss: 0.0872 - val_accuracy: 0.9858
Epoch 219/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9904 - val_loss: 0.0829 - val_accuracy: 0.9863
Epoch 220/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0531 - accuracy: 0.9905 - val_loss: 0.0910 - val_accuracy: 0.9859
Epoch 221/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9912 - val_loss: 0.0818 - val_accuracy: 0.9864
Epoch 222/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0498 - accuracy: 0.9908 - val_loss: 0.0837 - val_accuracy: 0.9860
Epoch 223/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9908 - val_loss: 0.0755 - val_accuracy: 0.9869
Epoch 224/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0519 - accuracy: 0.9910 - val_loss: 0.0771 - val_accuracy: 0.9862
Epoch 225/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0473 - accuracy: 0.9919 - val_loss: 0.0787 - val_accuracy: 0.9853
Epoch 226/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0437 - accuracy: 0.9923 - val_loss: 0.0839 - val_accuracy: 0.9870
Epoch 227/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0581 - accuracy: 0.9907 - val_loss: 0.0802 - val_accuracy: 0.9865
Epoch 228/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9905 - val_loss: 0.0794 - val_accuracy: 0.9865
Epoch 229/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0485 - accuracy: 0.9909 - val_loss: 0.0866 - val_accuracy: 0.9864
Epoch 230/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0586 - accuracy: 0.9909 - val_loss: 0.0920 - val_accuracy: 0.9851
Epoch 231/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0572 - accuracy: 0.9905 - val_loss: 0.0876 - val_accuracy: 0.9853
Epoch 232/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0577 - accuracy: 0.9908 - val_loss: 0.0776 - val_accuracy: 0.9861
Epoch 233/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0499 - accuracy: 0.9907 - val_loss: 0.0833 - val_accuracy: 0.9867
Epoch 234/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0471 - accuracy: 0.9919 - val_loss: 0.0866 - val_accuracy: 0.9864
Epoch 235/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0591 - accuracy: 0.9903 - val_loss: 0.0763 - val_accuracy: 0.9869
Epoch 236/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0505 - accuracy: 0.9903 - val_loss: 0.0846 - val_accuracy: 0.9880
Epoch 237/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0585 - accuracy: 0.9902 - val_loss: 0.0726 - val_accuracy: 0.9870
Epoch 238/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0617 - accuracy: 0.9903 - val_loss: 0.0748 - val_accuracy: 0.9872
Epoch 239/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0536 - accuracy: 0.9909 - val_loss: 0.0727 - val_accuracy: 0.9875
Epoch 240/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0595 - accuracy: 0.9905 - val_loss: 0.0717 - val_accuracy: 0.9869
Epoch 241/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9914 - val_loss: 0.0802 - val_accuracy: 0.9867
Epoch 242/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9904 - val_loss: 0.0857 - val_accuracy: 0.9871
Epoch 243/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0525 - accuracy: 0.9908 - val_loss: 0.0699 - val_accuracy: 0.9869
Epoch 244/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9914 - val_loss: 0.0898 - val_accuracy: 0.9869
Epoch 245/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0504 - accuracy: 0.9915 - val_loss: 0.0762 - val_accuracy: 0.9869
Epoch 246/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0479 - accuracy: 0.9914 - val_loss: 0.0809 - val_accuracy: 0.9878
Epoch 247/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9908 - val_loss: 0.0842 - val_accuracy: 0.9869
Epoch 248/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0467 - accuracy: 0.9918 - val_loss: 0.0795 - val_accuracy: 0.9876
Epoch 249/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0779 - accuracy: 0.9904 - val_loss: 0.0895 - val_accuracy: 0.9869
Epoch 250/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0595 - accuracy: 0.9905 - val_loss: 0.0927 - val_accuracy: 0.9872
Epoch 251/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0544 - accuracy: 0.9907 - val_loss: 0.0774 - val_accuracy: 0.9882
Epoch 252/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0483 - accuracy: 0.9915 - val_loss: 0.0740 - val_accuracy: 0.9873
Epoch 253/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0538 - accuracy: 0.9903 - val_loss: 0.0865 - val_accuracy: 0.9865
Epoch 254/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0539 - accuracy: 0.9906 - val_loss: 0.0967 - val_accuracy: 0.9856
Epoch 255/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0589 - accuracy: 0.9904 - val_loss: 0.0899 - val_accuracy: 0.9878
Epoch 256/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0461 - accuracy: 0.9910 - val_loss: 0.0733 - val_accuracy: 0.9867
Epoch 257/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0565 - accuracy: 0.9911 - val_loss: 0.0869 - val_accuracy: 0.9871
Epoch 258/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9909 - val_loss: 0.0819 - val_accuracy: 0.9878
Epoch 259/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.9916 - val_loss: 0.0735 - val_accuracy: 0.9871
Epoch 260/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9914 - val_loss: 0.0938 - val_accuracy: 0.9872
Epoch 261/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0631 - accuracy: 0.9892 - val_loss: 0.0939 - val_accuracy: 0.9860
Epoch 262/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9905 - val_loss: 0.0839 - val_accuracy: 0.9880
Epoch 263/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0462 - accuracy: 0.9913 - val_loss: 0.0788 - val_accuracy: 0.9874
Epoch 264/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0563 - accuracy: 0.9910 - val_loss: 0.0938 - val_accuracy: 0.9874
Epoch 265/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9912 - val_loss: 0.0755 - val_accuracy: 0.9883
Epoch 266/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0611 - accuracy: 0.9901 - val_loss: 0.0796 - val_accuracy: 0.9874
Epoch 267/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0458 - accuracy: 0.9918 - val_loss: 0.0750 - val_accuracy: 0.9877
Epoch 268/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0525 - accuracy: 0.9913 - val_loss: 0.0780 - val_accuracy: 0.9876
Epoch 269/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0494 - accuracy: 0.9909 - val_loss: 0.0847 - val_accuracy: 0.9866
Epoch 270/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0496 - accuracy: 0.9909 - val_loss: 0.0965 - val_accuracy: 0.9855
Epoch 271/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0502 - accuracy: 0.9917 - val_loss: 0.0939 - val_accuracy: 0.9861
Epoch 272/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0504 - accuracy: 0.9913 - val_loss: 0.0918 - val_accuracy: 0.9872
Epoch 273/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0462 - accuracy: 0.9917 - val_loss: 0.0929 - val_accuracy: 0.9880
Epoch 274/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0552 - accuracy: 0.9914 - val_loss: 0.0749 - val_accuracy: 0.9866
Epoch 275/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0525 - accuracy: 0.9918 - val_loss: 0.0894 - val_accuracy: 0.9866
Epoch 276/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0665 - accuracy: 0.9905 - val_loss: 0.0892 - val_accuracy: 0.9869
Epoch 277/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0586 - accuracy: 0.9907 - val_loss: 0.0762 - val_accuracy: 0.9872
Epoch 278/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0495 - accuracy: 0.9911 - val_loss: 0.0822 - val_accuracy: 0.9866
Epoch 279/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0437 - accuracy: 0.9917 - val_loss: 0.0781 - val_accuracy: 0.9862
Epoch 280/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0483 - accuracy: 0.9914 - val_loss: 0.0823 - val_accuracy: 0.9867
Epoch 281/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0592 - accuracy: 0.9922 - val_loss: 0.0918 - val_accuracy: 0.9876
Epoch 282/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0675 - accuracy: 0.9912 - val_loss: 0.0937 - val_accuracy: 0.9875
Epoch 283/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9913 - val_loss: 0.0816 - val_accuracy: 0.9878
Epoch 284/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0523 - accuracy: 0.9919 - val_loss: 0.0866 - val_accuracy: 0.9869
Epoch 285/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0470 - accuracy: 0.9913 - val_loss: 0.0841 - val_accuracy: 0.9865
Epoch 286/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0519 - accuracy: 0.9916 - val_loss: 0.0994 - val_accuracy: 0.9869
Epoch 287/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0463 - accuracy: 0.9915 - val_loss: 0.0868 - val_accuracy: 0.9870
Epoch 288/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0518 - accuracy: 0.9913 - val_loss: 0.0769 - val_accuracy: 0.9867
Epoch 289/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9918 - val_loss: 0.0756 - val_accuracy: 0.9874
Epoch 290/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0772 - accuracy: 0.9907 - val_loss: 0.0807 - val_accuracy: 0.9861
Epoch 291/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9909 - val_loss: 0.1009 - val_accuracy: 0.9861
Epoch 292/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0599 - accuracy: 0.9907 - val_loss: 0.0779 - val_accuracy: 0.9868
Epoch 293/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0535 - accuracy: 0.9912 - val_loss: 0.0880 - val_accuracy: 0.9867
Epoch 294/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0614 - accuracy: 0.9903 - val_loss: 0.0870 - val_accuracy: 0.9864
Epoch 295/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0539 - accuracy: 0.9910 - val_loss: 0.0885 - val_accuracy: 0.9859
Epoch 296/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9915 - val_loss: 0.0795 - val_accuracy: 0.9864
Epoch 297/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0617 - accuracy: 0.9912 - val_loss: 0.0851 - val_accuracy: 0.9861
Epoch 298/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0632 - accuracy: 0.9907 - val_loss: 0.0880 - val_accuracy: 0.9863
Epoch 299/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9912 - val_loss: 0.1030 - val_accuracy: 0.9864
Epoch 300/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0431 - accuracy: 0.9925 - val_loss: 0.0932 - val_accuracy: 0.9871
Epoch 301/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0504 - accuracy: 0.9913 - val_loss: 0.0898 - val_accuracy: 0.9883
Epoch 302/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0427 - accuracy: 0.9923 - val_loss: 0.1005 - val_accuracy: 0.9876
Epoch 303/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0402 - accuracy: 0.9923 - val_loss: 0.0861 - val_accuracy: 0.9881
Epoch 304/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0488 - accuracy: 0.9919 - val_loss: 0.0791 - val_accuracy: 0.9868
Epoch 305/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0501 - accuracy: 0.9919 - val_loss: 0.0993 - val_accuracy: 0.9872
Epoch 306/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0553 - accuracy: 0.9918 - val_loss: 0.0954 - val_accuracy: 0.9872
Epoch 307/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0779 - accuracy: 0.9911 - val_loss: 0.0840 - val_accuracy: 0.9861
Epoch 308/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0568 - accuracy: 0.9902 - val_loss: 0.0894 - val_accuracy: 0.9883
Epoch 309/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0515 - accuracy: 0.9908 - val_loss: 0.0912 - val_accuracy: 0.9866
Epoch 310/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0424 - accuracy: 0.9921 - val_loss: 0.0938 - val_accuracy: 0.9875
Epoch 311/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0554 - accuracy: 0.9919 - val_loss: 0.0887 - val_accuracy: 0.9881
Epoch 312/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0447 - accuracy: 0.9923 - val_loss: 0.1094 - val_accuracy: 0.9867
Epoch 313/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0544 - accuracy: 0.9919 - val_loss: 0.0923 - val_accuracy: 0.9879
Epoch 314/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0585 - accuracy: 0.9911 - val_loss: 0.0886 - val_accuracy: 0.9880
Epoch 315/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0816 - accuracy: 0.9911 - val_loss: 0.0923 - val_accuracy: 0.9870
Epoch 316/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0663 - accuracy: 0.9905 - val_loss: 0.0960 - val_accuracy: 0.9870
Epoch 317/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9912 - val_loss: 0.0895 - val_accuracy: 0.9875
Epoch 318/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0454 - accuracy: 0.9920 - val_loss: 0.0989 - val_accuracy: 0.9877
Epoch 319/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0507 - accuracy: 0.9919 - val_loss: 0.0911 - val_accuracy: 0.9875
Epoch 320/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0491 - accuracy: 0.9918 - val_loss: 0.0996 - val_accuracy: 0.9881
Epoch 321/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0435 - accuracy: 0.9921 - val_loss: 0.1045 - val_accuracy: 0.9873
Epoch 322/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9913 - val_loss: 0.1012 - val_accuracy: 0.9868
Epoch 323/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0542 - accuracy: 0.9920 - val_loss: 0.1033 - val_accuracy: 0.9862
Epoch 324/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0578 - accuracy: 0.9918 - val_loss: 0.0973 - val_accuracy: 0.9869
Epoch 325/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1249 - accuracy: 0.9902 - val_loss: 0.0819 - val_accuracy: 0.9880
Epoch 326/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0490 - accuracy: 0.9904 - val_loss: 0.0904 - val_accuracy: 0.9872
Epoch 327/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0523 - accuracy: 0.9913 - val_loss: 0.0856 - val_accuracy: 0.9879
Epoch 328/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0626 - accuracy: 0.9917 - val_loss: 0.0915 - val_accuracy: 0.9878
Epoch 329/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0805 - accuracy: 0.9913 - val_loss: 0.0997 - val_accuracy: 0.9875
Epoch 330/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0527 - accuracy: 0.9903 - val_loss: 0.0841 - val_accuracy: 0.9881
Epoch 331/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0506 - accuracy: 0.9920 - val_loss: 0.0915 - val_accuracy: 0.9886
Epoch 332/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0914 - accuracy: 0.9920 - val_loss: 0.0953 - val_accuracy: 0.9883
Epoch 333/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0538 - accuracy: 0.9915 - val_loss: 0.1008 - val_accuracy: 0.9883
Epoch 334/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0565 - accuracy: 0.9916 - val_loss: 0.0867 - val_accuracy: 0.9885
Epoch 335/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0509 - accuracy: 0.9912 - val_loss: 0.0876 - val_accuracy: 0.9876
Epoch 336/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0509 - accuracy: 0.9917 - val_loss: 0.0820 - val_accuracy: 0.9880
Epoch 337/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0416 - accuracy: 0.9926 - val_loss: 0.1034 - val_accuracy: 0.9882
Epoch 338/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0482 - accuracy: 0.9922 - val_loss: 0.0744 - val_accuracy: 0.9877
Epoch 339/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0542 - accuracy: 0.9923 - val_loss: 0.0893 - val_accuracy: 0.9869
Epoch 340/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0497 - accuracy: 0.9922 - val_loss: 0.0929 - val_accuracy: 0.9874
Epoch 341/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0471 - accuracy: 0.9922 - val_loss: 0.0854 - val_accuracy: 0.9881
Epoch 342/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0564 - accuracy: 0.9916 - val_loss: 0.0984 - val_accuracy: 0.9884
Epoch 343/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0624 - accuracy: 0.9906 - val_loss: 0.1047 - val_accuracy: 0.9882
Epoch 344/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0429 - accuracy: 0.9920 - val_loss: 0.0863 - val_accuracy: 0.9877
Epoch 345/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0597 - accuracy: 0.9922 - val_loss: 0.0736 - val_accuracy: 0.9885
Epoch 346/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0567 - accuracy: 0.9917 - val_loss: 0.0880 - val_accuracy: 0.9875
Epoch 347/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0491 - accuracy: 0.9913 - val_loss: 0.0826 - val_accuracy: 0.9869
Epoch 348/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0458 - accuracy: 0.9916 - val_loss: 0.1060 - val_accuracy: 0.9871
Epoch 349/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0440 - accuracy: 0.9924 - val_loss: 0.1251 - val_accuracy: 0.9874
Epoch 350/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0565 - accuracy: 0.9924 - val_loss: 0.0974 - val_accuracy: 0.9878
Epoch 351/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0521 - accuracy: 0.9917 - val_loss: 0.1117 - val_accuracy: 0.9866
Epoch 352/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0615 - accuracy: 0.9911 - val_loss: 0.1331 - val_accuracy: 0.9871
Epoch 353/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0536 - accuracy: 0.9910 - val_loss: 0.1073 - val_accuracy: 0.9869
Epoch 354/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0481 - accuracy: 0.9924 - val_loss: 0.0826 - val_accuracy: 0.9876
Epoch 355/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0418 - accuracy: 0.9923 - val_loss: 0.0935 - val_accuracy: 0.9869
Epoch 356/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0481 - accuracy: 0.9920 - val_loss: 0.1087 - val_accuracy: 0.9879
Epoch 357/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0614 - accuracy: 0.9915 - val_loss: 0.0867 - val_accuracy: 0.9872
Epoch 358/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0682 - accuracy: 0.9903 - val_loss: 0.1048 - val_accuracy: 0.9869
Epoch 359/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0508 - accuracy: 0.9918 - val_loss: 0.1055 - val_accuracy: 0.9872
Epoch 360/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0437 - accuracy: 0.9920 - val_loss: 0.0962 - val_accuracy: 0.9868
Epoch 361/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0657 - accuracy: 0.9918 - val_loss: 0.0961 - val_accuracy: 0.9866
Epoch 362/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0748 - accuracy: 0.9902 - val_loss: 0.1036 - val_accuracy: 0.9883
Epoch 363/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0746 - accuracy: 0.9902 - val_loss: 0.0903 - val_accuracy: 0.9878
Epoch 364/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0477 - accuracy: 0.9916 - val_loss: 0.0794 - val_accuracy: 0.9888
Epoch 365/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0458 - accuracy: 0.9925 - val_loss: 0.0788 - val_accuracy: 0.9875
Epoch 366/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0469 - accuracy: 0.9927 - val_loss: 0.0870 - val_accuracy: 0.9883
Epoch 367/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0543 - accuracy: 0.9916 - val_loss: 0.0983 - val_accuracy: 0.9864
Epoch 368/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0440 - accuracy: 0.9927 - val_loss: 0.0835 - val_accuracy: 0.9879
Epoch 369/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0587 - accuracy: 0.9915 - val_loss: 0.0783 - val_accuracy: 0.9873
Epoch 370/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0782 - accuracy: 0.9912 - val_loss: 0.0966 - val_accuracy: 0.9873
Epoch 371/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0476 - accuracy: 0.9919 - val_loss: 0.0803 - val_accuracy: 0.9876
Epoch 372/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0485 - accuracy: 0.9925 - val_loss: 0.0910 - val_accuracy: 0.9877
Epoch 373/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0552 - accuracy: 0.9914 - val_loss: 0.1070 - val_accuracy: 0.9868
Epoch 374/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0481 - accuracy: 0.9919 - val_loss: 0.0915 - val_accuracy: 0.9882
Epoch 375/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0478 - accuracy: 0.9925 - val_loss: 0.0944 - val_accuracy: 0.9878
Epoch 376/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0483 - accuracy: 0.9921 - val_loss: 0.0863 - val_accuracy: 0.9877
Epoch 377/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0494 - accuracy: 0.9918 - val_loss: 0.0935 - val_accuracy: 0.9869
Epoch 378/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0578 - accuracy: 0.9912 - val_loss: 0.0911 - val_accuracy: 0.9873
Epoch 379/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0545 - accuracy: 0.9912 - val_loss: 0.0852 - val_accuracy: 0.9878
Epoch 380/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0424 - accuracy: 0.9933 - val_loss: 0.1014 - val_accuracy: 0.9874
Epoch 381/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0601 - accuracy: 0.9922 - val_loss: 0.0810 - val_accuracy: 0.9877
Epoch 382/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0573 - accuracy: 0.9913 - val_loss: 0.0818 - val_accuracy: 0.9874
Epoch 383/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0507 - accuracy: 0.9921 - val_loss: 0.1079 - val_accuracy: 0.9887
Epoch 384/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0575 - accuracy: 0.9917 - val_loss: 0.0927 - val_accuracy: 0.9880
Epoch 385/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0465 - accuracy: 0.9921 - val_loss: 0.0948 - val_accuracy: 0.9873
Epoch 386/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0519 - accuracy: 0.9921 - val_loss: 0.0893 - val_accuracy: 0.9876
Epoch 387/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0428 - accuracy: 0.9923 - val_loss: 0.0895 - val_accuracy: 0.9868
Epoch 388/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0574 - accuracy: 0.9916 - val_loss: 0.0854 - val_accuracy: 0.9875
Epoch 389/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0470 - accuracy: 0.9923 - val_loss: 0.0942 - val_accuracy: 0.9873
Epoch 390/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0555 - accuracy: 0.9917 - val_loss: 0.0895 - val_accuracy: 0.9876
Epoch 391/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0615 - accuracy: 0.9910 - val_loss: 0.1044 - val_accuracy: 0.9885
Epoch 392/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0777 - accuracy: 0.9905 - val_loss: 0.1180 - val_accuracy: 0.9867
Epoch 393/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0739 - accuracy: 0.9904 - val_loss: 0.1006 - val_accuracy: 0.9870
Epoch 394/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0829 - accuracy: 0.9906 - val_loss: 0.0889 - val_accuracy: 0.9878
Epoch 395/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0595 - accuracy: 0.9907 - val_loss: 0.0963 - val_accuracy: 0.9869
Epoch 396/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0674 - accuracy: 0.9910 - val_loss: 0.0971 - val_accuracy: 0.9866
Epoch 397/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9913 - val_loss: 0.0949 - val_accuracy: 0.9865
Epoch 398/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0438 - accuracy: 0.9922 - val_loss: 0.0820 - val_accuracy: 0.9873
Epoch 399/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0585 - accuracy: 0.9916 - val_loss: 0.0967 - val_accuracy: 0.9870
Epoch 400/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0573 - accuracy: 0.9918 - val_loss: 0.0956 - val_accuracy: 0.9860
Epoch 401/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0624 - accuracy: 0.9922 - val_loss: 0.0845 - val_accuracy: 0.9866
Epoch 402/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0531 - accuracy: 0.9916 - val_loss: 0.1288 - val_accuracy: 0.9879
Epoch 403/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0477 - accuracy: 0.9923 - val_loss: 0.0999 - val_accuracy: 0.9879
Epoch 404/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9919 - val_loss: 0.1138 - val_accuracy: 0.9866
Epoch 405/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0578 - accuracy: 0.9922 - val_loss: 0.0997 - val_accuracy: 0.9871
Epoch 406/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0683 - accuracy: 0.9901 - val_loss: 0.1156 - val_accuracy: 0.9871
Epoch 407/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0554 - accuracy: 0.9915 - val_loss: 0.1025 - val_accuracy: 0.9868
Epoch 408/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0483 - accuracy: 0.9917 - val_loss: 0.1084 - val_accuracy: 0.9875
Epoch 409/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0588 - accuracy: 0.9909 - val_loss: 0.1243 - val_accuracy: 0.9861
Epoch 410/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0655 - accuracy: 0.9905 - val_loss: 0.1162 - val_accuracy: 0.9871
Epoch 411/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0705 - accuracy: 0.9915 - val_loss: 0.1108 - val_accuracy: 0.9871
Epoch 412/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0762 - accuracy: 0.9909 - val_loss: 0.1126 - val_accuracy: 0.9870
Epoch 413/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0528 - accuracy: 0.9916 - val_loss: 0.1126 - val_accuracy: 0.9873
Epoch 414/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9918 - val_loss: 0.1133 - val_accuracy: 0.9876
Epoch 415/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0463 - accuracy: 0.9917 - val_loss: 0.1087 - val_accuracy: 0.9867
Epoch 416/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0546 - accuracy: 0.9915 - val_loss: 0.0921 - val_accuracy: 0.9872
Epoch 417/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0561 - accuracy: 0.9924 - val_loss: 0.1213 - val_accuracy: 0.9872
Epoch 418/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0523 - accuracy: 0.9917 - val_loss: 0.0921 - val_accuracy: 0.9860
Epoch 419/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0555 - accuracy: 0.9910 - val_loss: 0.0899 - val_accuracy: 0.9875
Epoch 420/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0585 - accuracy: 0.9919 - val_loss: 0.0960 - val_accuracy: 0.9871
Epoch 421/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0446 - accuracy: 0.9927 - val_loss: 0.1071 - val_accuracy: 0.9876
Epoch 422/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0785 - accuracy: 0.9922 - val_loss: 0.0868 - val_accuracy: 0.9875
Epoch 423/500
469/469 [==============================] - 2s 5ms/step - loss: 0.0498 - accuracy: 0.9915 - val_loss: 0.0905 - val_accuracy: 0.9885
Epoch 424/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0523 - accuracy: 0.9915 - val_loss: 0.0846 - val_accuracy: 0.9880
Epoch 425/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0494 - accuracy: 0.9928 - val_loss: 0.0909 - val_accuracy: 0.9876
Epoch 426/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0529 - accuracy: 0.9917 - val_loss: 0.1041 - val_accuracy: 0.9828
Epoch 427/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0516 - accuracy: 0.9922 - val_loss: 0.0982 - val_accuracy: 0.9880
Epoch 428/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9926 - val_loss: 0.0785 - val_accuracy: 0.9885
Epoch 429/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9916 - val_loss: 0.0950 - val_accuracy: 0.9881
Epoch 430/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0605 - accuracy: 0.9909 - val_loss: 0.1117 - val_accuracy: 0.9879
Epoch 431/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0600 - accuracy: 0.9911 - val_loss: 0.1007 - val_accuracy: 0.9876
Epoch 432/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0739 - accuracy: 0.9913 - val_loss: 0.1002 - val_accuracy: 0.9869
Epoch 433/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9920 - val_loss: 0.0899 - val_accuracy: 0.9871
Epoch 434/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0482 - accuracy: 0.9924 - val_loss: 0.1103 - val_accuracy: 0.9865
Epoch 435/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0493 - accuracy: 0.9927 - val_loss: 0.0979 - val_accuracy: 0.9870
Epoch 436/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9918 - val_loss: 0.1004 - val_accuracy: 0.9880
Epoch 437/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0451 - accuracy: 0.9921 - val_loss: 0.0955 - val_accuracy: 0.9867
Epoch 438/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0547 - accuracy: 0.9920 - val_loss: 0.1086 - val_accuracy: 0.9867
Epoch 439/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0482 - accuracy: 0.9917 - val_loss: 0.0912 - val_accuracy: 0.9871
Epoch 440/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0667 - accuracy: 0.9912 - val_loss: 0.1098 - val_accuracy: 0.9864
Epoch 441/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0506 - accuracy: 0.9921 - val_loss: 0.1144 - val_accuracy: 0.9850
Epoch 442/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9911 - val_loss: 0.1218 - val_accuracy: 0.9869
Epoch 443/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1562 - accuracy: 0.9910 - val_loss: 0.1236 - val_accuracy: 0.9875
Epoch 444/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0721 - accuracy: 0.9919 - val_loss: 0.1253 - val_accuracy: 0.9888
Epoch 445/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0603 - accuracy: 0.9912 - val_loss: 0.1260 - val_accuracy: 0.9865
Epoch 446/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0543 - accuracy: 0.9911 - val_loss: 0.1131 - val_accuracy: 0.9871
Epoch 447/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0560 - accuracy: 0.9914 - val_loss: 0.1055 - val_accuracy: 0.9875
Epoch 448/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0488 - accuracy: 0.9923 - val_loss: 0.1040 - val_accuracy: 0.9876
Epoch 449/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0517 - accuracy: 0.9919 - val_loss: 0.0916 - val_accuracy: 0.9876
Epoch 450/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1061 - accuracy: 0.9921 - val_loss: 0.0985 - val_accuracy: 0.9869
Epoch 451/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0498 - accuracy: 0.9920 - val_loss: 0.1043 - val_accuracy: 0.9875
Epoch 452/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0711 - accuracy: 0.9916 - val_loss: 0.1023 - val_accuracy: 0.9873
Epoch 453/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0615 - accuracy: 0.9916 - val_loss: 0.0927 - val_accuracy: 0.9881
Epoch 454/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0535 - accuracy: 0.9915 - val_loss: 0.0796 - val_accuracy: 0.9883
Epoch 455/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0468 - accuracy: 0.9919 - val_loss: 0.1241 - val_accuracy: 0.9885
Epoch 456/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0624 - accuracy: 0.9916 - val_loss: 0.0981 - val_accuracy: 0.9878
Epoch 457/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0538 - accuracy: 0.9919 - val_loss: 0.0998 - val_accuracy: 0.9884
Epoch 458/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0495 - accuracy: 0.9918 - val_loss: 0.1100 - val_accuracy: 0.9871
Epoch 459/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9921 - val_loss: 0.0930 - val_accuracy: 0.9879
Epoch 460/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0538 - accuracy: 0.9919 - val_loss: 0.0889 - val_accuracy: 0.9874
Epoch 461/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0491 - accuracy: 0.9922 - val_loss: 0.0981 - val_accuracy: 0.9877
Epoch 462/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0463 - accuracy: 0.9922 - val_loss: 0.1004 - val_accuracy: 0.9881
Epoch 463/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0462 - accuracy: 0.9923 - val_loss: 0.0992 - val_accuracy: 0.9854
Epoch 464/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0405 - accuracy: 0.9930 - val_loss: 0.1144 - val_accuracy: 0.9860
Epoch 465/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0723 - accuracy: 0.9928 - val_loss: 0.1198 - val_accuracy: 0.9869
Epoch 466/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0582 - accuracy: 0.9913 - val_loss: 0.1192 - val_accuracy: 0.9864
Epoch 467/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0435 - accuracy: 0.9920 - val_loss: 0.1355 - val_accuracy: 0.9872
Epoch 468/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0577 - accuracy: 0.9917 - val_loss: 0.1092 - val_accuracy: 0.9866
Epoch 469/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0520 - accuracy: 0.9920 - val_loss: 0.1024 - val_accuracy: 0.9875
Epoch 470/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0487 - accuracy: 0.9922 - val_loss: 0.0923 - val_accuracy: 0.9876
Epoch 471/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0424 - accuracy: 0.9926 - val_loss: 0.1076 - val_accuracy: 0.9869
Epoch 472/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0495 - accuracy: 0.9919 - val_loss: 0.1008 - val_accuracy: 0.9878
Epoch 473/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0442 - accuracy: 0.9927 - val_loss: 0.0977 - val_accuracy: 0.9881
Epoch 474/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0580 - accuracy: 0.9920 - val_loss: 0.1003 - val_accuracy: 0.9869
Epoch 475/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0489 - accuracy: 0.9924 - val_loss: 0.0990 - val_accuracy: 0.9874
Epoch 476/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0505 - accuracy: 0.9923 - val_loss: 0.1158 - val_accuracy: 0.9876
Epoch 477/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0513 - accuracy: 0.9921 - val_loss: 0.1089 - val_accuracy: 0.9877
Epoch 478/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0549 - accuracy: 0.9927 - val_loss: 0.1085 - val_accuracy: 0.9876
Epoch 479/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0542 - accuracy: 0.9922 - val_loss: 0.1029 - val_accuracy: 0.9868
Epoch 480/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0594 - accuracy: 0.9911 - val_loss: 0.0889 - val_accuracy: 0.9882
Epoch 481/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0571 - accuracy: 0.9915 - val_loss: 0.0940 - val_accuracy: 0.9874
Epoch 482/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0498 - accuracy: 0.9920 - val_loss: 0.0984 - val_accuracy: 0.9877
Epoch 483/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0649 - accuracy: 0.9920 - val_loss: 0.0939 - val_accuracy: 0.9880
Epoch 484/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0561 - accuracy: 0.9917 - val_loss: 0.0878 - val_accuracy: 0.9881
Epoch 485/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0497 - accuracy: 0.9922 - val_loss: 0.1071 - val_accuracy: 0.9881
Epoch 486/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0578 - accuracy: 0.9916 - val_loss: 0.1034 - val_accuracy: 0.9871
Epoch 487/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0499 - accuracy: 0.9921 - val_loss: 0.0936 - val_accuracy: 0.9875
Epoch 488/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0635 - accuracy: 0.9913 - val_loss: 0.1096 - val_accuracy: 0.9868
Epoch 489/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0483 - accuracy: 0.9924 - val_loss: 0.1026 - val_accuracy: 0.9887
Epoch 490/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0563 - accuracy: 0.9922 - val_loss: 0.1009 - val_accuracy: 0.9874
Epoch 491/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0544 - accuracy: 0.9923 - val_loss: 0.1077 - val_accuracy: 0.9883
Epoch 492/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0492 - accuracy: 0.9931 - val_loss: 0.1044 - val_accuracy: 0.9880
Epoch 493/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0557 - accuracy: 0.9921 - val_loss: 0.0881 - val_accuracy: 0.9882
Epoch 494/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0580 - accuracy: 0.9924 - val_loss: 0.0956 - val_accuracy: 0.9884
Epoch 495/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0553 - accuracy: 0.9919 - val_loss: 0.1043 - val_accuracy: 0.9877
Epoch 496/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0470 - accuracy: 0.9920 - val_loss: 0.0857 - val_accuracy: 0.9873
Epoch 497/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0428 - accuracy: 0.9928 - val_loss: 0.0771 - val_accuracy: 0.9879
Epoch 498/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0433 - accuracy: 0.9927 - val_loss: 0.0844 - val_accuracy: 0.9881
Epoch 499/500
469/469 [==============================] - 2s 4ms/step - loss: 0.0645 - accuracy: 0.9915 - val_loss: 0.0935 - val_accuracy: 0.9876
Epoch 500/500
469/469 [==============================] - 2s 4ms/step - loss: 0.1125 - accuracy: 0.9918 - val_loss: 0.0836 - val_accuracy: 0.9874

Visualize Dropout

In [ ]:
train_err = history.history['loss']
test_err = history.history['val_loss']
train_errd = historyd.history['loss']
test_errd = historyd.history['val_loss']


epochs = range(0,500)
plt.figure(figsize=(20,10))
plt.plot(epochs, train_err, 'b', label='No Dropout: Train Loss * 1000', linewidth=3)
plt.plot(epochs, test_err, 'b', label='No Dropout: Test Loss')
plt.plot(epochs, train_errd, 'r', label='Dropout: Train Loss * 1000', linewidth=3)
plt.plot(epochs, test_errd, 'r', label='Dropout: Test Loss')
plt.title('1024X5 Logistic')
plt.xlabel('Epoch')
plt.ylabel('Cross Entropy Error')
plt.legend()
plt.show()
In [ ]:
train_err = history1.history['loss']
test_err = history1.history['val_loss']
train_errd = historyd1.history['loss']
test_errd = historyd1.history['val_loss']


epochs = range(0,500)
plt.figure(figsize=(20,10))
plt.axis([None, None, 0, 1])
plt.plot(epochs, train_err, 'b', label='No Dropout: Train Loss * 1000', linewidth=3)
plt.plot(epochs, test_err, 'b', label='No Dropout: Test Loss')
plt.plot(epochs, train_errd, 'r', label='Dropout: Train Loss * 1000', linewidth=3)
plt.plot(epochs, test_errd, 'r', label='Dropout: Test Loss')
plt.title('1024X5 ReLU')
plt.xlabel('Epoch')
plt.ylabel('Cross Entropy Error')
plt.legend()
plt.show()
In [ ]: